Description:FIELD OF INVENTION
[0001] The embodiments of the present disclosure generally relate to a field of image processing systems. More particularly, the present disclosure relates to a method and a system for enhancing, augmenting and extending an extended reality scene.

BACKGROUND
[0002] The following description of the related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of the prior art.
[0003] Augmented reality and virtual reality will radically change the way humans can work, play and communicate. Extended Reality (XR) refers to all combined real and virtual environments and man-machine interactions, and is therefore, to be understood as the “reservoir” for representative forms such as Augmented Reality (AR) and Virtual Reality (VR) and the interpolated areas between them. XR provides an immersive sensory experience. In extended reality, when one enters into another world visually, sonically, or even haptically, the border between reality and the simulated environment blurs. Artificial Intelligence (AI) or Machine learning (ML) or deep learning (DL) enabled extended reality (XR) can further open exciting new opportunities of a digital future, where the augmented or virtual realities are not just realities, but are intelligent ones. With integration AI / ML / DL based capabilities, the XR tools will have the potential to grow and become more intelligent over time. Additionally, as stated earlier, such an integration of AI and XR can also make extended reality a more realistic opportunity relevant for everyone’s use i.e adoption en-masse. As more and more data get collected through extended reality interactions, the devices and the software in them will become more evolved, learning the context and details of the exhaustive yet specific interactive situations. Through intelligent systems, it’s easier to track things like gestures, and eye movements that could make XR environments more immersive.
[0004] There are multiple Deep Learning approaches that can be deployed to generate an XR scene. They include convolution neural network (CNN), recurrent neural network (RNN), Generative adversarial networks (GAN), variational autoencoders (VAE), Deep Boltzmann Machine, PixelRNN, and the like. However, existing systems do not provide an approach to image synthesis for rendering the relevant XR scenes / experiences to the users. Until date, the scalability was always first a function of the annotated data-sets available. The other aspects of scalability are the varying degree of immersions, the XR experience across devices and between users both co-located and distributed. Interaction between users and XR content is a crucial element of any immersive visualization environment. There is no way for the user to interact with the immersive world. Further, there is no authenticity / fidelity of the XR content and the realism of XR interaction. Furthermore, it has been demonstrated that the desired immersion in the XR environment may not be achieved if the interaction method is not easy to operate or if the novelty of the interface overshadows the content. Hence, balancing interaction, engagement, and content is very crucial for immersivity.
[0005] Therefore, there is a need for a method and a system for solving the shortcomings of the prior arts, by providing a method and a system for enhancing, augmenting and extending an extended reality scene.

SUMMARY
[0006] This section is provided to introduce certain objects and aspects of the present invention in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter. In order to overcome at least a few problems associated with the known solutions as provided in the previous section, an object of the present invention is to provide a technique that may be for context-aware query expansion in a computing environment.
[0007] It is an object of the present disclosure to provide a system and a method for enhancing, augmenting and extending an extended reality scene.
[0008] It is another object of the present disclosure to provide a system and a method for achieving the optimum balancing of the three variables, balancing interaction, engagement, and content ascertaining depth of immersivity experienced.
[0009] It is another object of the present disclosure to provide an approach to image synthesis for rendering the relevant XR scenes / experiences to the users.
[0010] It is another object of the present disclosure to provide a tool of interaction between the immersive environment, the user and the Artificial intelligence (AI) / Generative adversarial networks (GAN) based engine that provides the XR Construct.
[0011] It is another object of the present disclosure to provide a system and a method to vary the degree of immersion of the viewer with the XR Scene.
[0012] It is another object of the present disclosure to use GAN to do image synthesis by learning from the limited data and by using synthetic data for the relevant scene generation.
[0013] It is another object of the present disclosure to provide bidirectional interaction and maintain consistency with the real environment, virtual reconstructions of physical scenes are to be segmented semantically and adapted dynamically.
[0014] It is another object of the present disclosure to provide a scalable interaction technique for selection, manipulation, and navigation of 3D Objects and 2D annotations in a 3D space to let users intuitively switch between handheld and head-mounted displays.
[0015] It is another object of the present disclosure to provide an interaction technique through a tool for interface between the user and the XR scene that can aid varying degree of immersivity.
[0016] In an aspect, the present disclosure provides a system for generating and enhancing an extended reality (XR) scene in a computing environment. The system may include an emulating wearable device configured to be worn by a user and an extended reality (XR) scene slider operatively coupled to the emulating wearable device. In an embodiment, the emulating wearable device may include one or more depth camera sensors to capture one or more attributes of a surrounding scene of the user. The one or more depth sensors may be further operatively coupled to an artificial intelligence (AI) engine. The AI engine may be integrated inside the emulating wearable device and may be configured to generate one or more intensive visuals from the one or more attributes of a surrounding scene of the user. In another embodiment, the XR scene slider may include one or more processors coupled with a memory, the memory storing instructions which when executed by the one or more processors, may cause the XR scene slider to receive the one or more intensive visuals from the AI engine associated with the emulating wearable device and then extract a first set of attributes from the one or more intensive visuals received. The first set of attributes may pertain to one or more object boundaries, location and geometry of the one or more objects with respect to each other, annotation features, filters, colors, hues, lighting, shadow effects and motion tracker outputs obtained from the one or more dept camera sensors. The XR scene slider may be further configured to map the first set of attributes to one or more scenes based on a predefined set of instructions pertaining to techniques based on Generative Adversarial Networks (GAN); and compose one or more XR scenes based on the mapping of the first set of attributes to the one or more scenes. In an embodiment, the one or more XR scenes may be composed based on a plurality of immersion grades pertaining to states of immersion the user is taken through in the one or more XR scenes.
[0017] In an embodiment, the emulating wearable device further may include at least a six-degree-of-freedom (6DoF) headset and a controller. The headset and the controller comprise the one or more depth cameras, one or more sensor suites, one or more motion controllers and human interfaces.
[0018] In an embodiment, to generate the one or more intensive visuals, the AI engine integrated in the emulating wearable device further may include an Image Extractor, an Object Extractor, a Stipulator, an Object Studio, an Object Positioning module and a Scene Generator.
[0019] In an embodiment, to generate the one or more intensive visuals, the AI engine integrated in the emulating wearable device may be further configured to extract, using the image extractor, an image from the surrounding scene of the user, extract, using the object extractor, one or more objects from the image, process, using the stipulator, the one or more objects extracted, create, using the object studio, a new set of objects based on the processed one or more objects, position, using the object positioning module, the new set of objects created; and generate, by the scene generator the one or more intensive visuals.
[0020] In an embodiment, the AI engine may be further configured to correlate the user’s need to vary an XR experience to seamlessly combine real objects, virtual objects, annotation items and other elements that make the one or more intensive visuals.
[0021] In an embodiment, the XR Scene Slider may be a navigable element designed as a part of the emulating wearable device. The XR Scene Slider may be configured to maneuver in a generated XR Field of View of the user or through a hardware such as a mini protuberance provided as a part of the emulating wearable device’s physical form factor.
[0022] In an embodiment, the XR scene slider may move forward or backward based on waxing or waning of the XR scene correspondingly.
[0023] In an embodiment, the AI engine may be configured to titrate the growth or depletion of the XR Scene that the AI engine is compositing based on the forward or backward movement of the XR scene slider.
[0024] In an embodiment, the AI engine may be further configured to use convolutional neural network to generate an image with accurate retinal blur as soon as the eye of the user in the emulating wearable device moves over a scene and minimize one or more spatial dimensions of the image while fully preserving visual information.
[0025] In an embodiment, the XR Scene Slider may be further configured to decompose one or more XR scenes based on the mapping of the first set of attributes to the one or more scenes. The one or more XR scenes may be decomposed based on a plurality of immersion grades pertaining to states of immersion the user is taken through in the one or more XR scenes.
[0026] In an embodiment, the plurality of immersion grades may include at least five immersion states such as essential, enhanced, elaborate, enriched and epitome states of immersion that the user is taken through progressively.
[0027] In an embodiment, in the essential state, the XR scene may be composited with most minimal and essential overlay of the one or objects obtained from the surrounding scene. The enhanced state may be an improved variance of the XR scene factoring a predefine additional quantum of digital objects, while the elaborate state may be an advanced version of the XR scene that has the maximum quantum of digital objects overlay generated with the User as a central point of foci for a more complete immersive experiences with digital objects spanning 2D objects, 3D objects and text overlays. In the enriched state, the degree of immersion may be increased by a predefined value that involves both video and auditory faculties of the user by blending video and spatial audio, and the Epitome state may have the highest degree of immersion that involves a plurality of faculties of the one more objects spanning video, text, spatial audio, haptics and associated sensor stimulants centric technologies.
[0028] In an embodiment, the XR Scene Slider may be configured to complete one or more incomplete surrounding scene using the creative generation capabilities of GANs, enable lighting and reflections for texturing of an XR scene, superimpose dynamic information on the XR scene related to the XR scene elements and generate meta data on the XR scene based on real-time user and environment interactions.
[0029] In an aspect, the present disclosure provides a method for generating and enhancing an extended reality (XR) scene in a computing environment. The method may include the step of receiving, by one or more processors, the one or more intensive visuals from an artificial intelligence (AI) engine associated with an emulating wearable device. In an embodiment, the one or more processors may be associated with an XR scene slider operatively coupled to the emulating wearable device. In an embodiment, the emulating wearable device may be configured to be worn by a user. The emulating wearable device may further include one or more depth camera sensors to capture one or more attributes of a surrounding scene of the user, the one or more depth sensors may be further operatively coupled to the AI engine integrated inside the emulating wearable device. The AI engine may further include the step of generating one or more intensive visuals from the one or more attributes of a surrounding scene of the user. The method may further include the step of extracting, by the one or more processors, a first set of attributes from the one or more intensive visuals received, the first set of attributes pertaining to one or more object boundaries, location and geometry of the one or more objects with respect to each other, annotation features, filters, colors, hues, lighting, shadow effects and motion tracker outputs obtained from the one or more dept camera sensors. The method may further include the step of mapping, by the one or more processors, the first set of attributes to one or more scenes based on a predefined set of instructions pertaining to techniques based on Generative Adversarial Networks (GAN). Furthermore, the method may include the step of composing, by the one or more processors, one or more XR scenes based on the mapping of the first set of attributes to the one or more scenes. The one or more XR scenes are composed based on a plurality of immersion grades pertaining to states of immersion the user is taken through in the one or more XR scenes.
[0030] In an aspect, the present disclosure provides a device for generating and enhancing an extended reality (XR) scene in a computing environment. The device may include an emulating wearable device configured to be worn by a user and an extended reality (XR) scene slider operatively coupled to the emulating wearable device. In an embodiment, the emulating wearable device may include one or more depth camera sensors to capture one or more attributes of a surrounding scene of the user. The one or more depth sensors may be further operatively coupled to an artificial intelligence (AI) engine. The AI engine may be integrated inside the emulating wearable device and may be configured to generate one or more intensive visuals from the one or more attributes of a surrounding scene of the user. In another embodiment, the XR scene slider may include one or more processors coupled with a memory, the memory storing instructions which when executed by the one or more processors, may cause the XR scene slider to receive the one or more intensive visuals from the AI engine associated with the emulating wearable device and then extract a first set of attributes from the one or more intensive visuals received. The first set of attributes may pertain to one or more object boundaries, location and geometry of the one or more objects with respect to each other, annotation features, filters, colors, hues, lighting, shadow effects and motion tracker outputs obtained from the one or more dept camera sensors. The XR scene slider may be further configured to map the first set of attributes to one or more scenes based on a predefined set of instructions pertaining to techniques based on Generative Adversarial Networks (GAN); and compose one or more XR scenes based on the mapping of the first set of attributes to the one or more scenes. In an embodiment, the one or more XR scenes may be composed based on a plurality of immersion grades pertaining to states of immersion the user is taken through in the one or more XR scenes.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS
[0031] The accompanying drawings, which are incorporated herein, and constitute a part of this invention, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present invention. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry/sub components of each component. It will be appreciated by those skilled in the art that the invention of such drawings includes the invention of electrical components, electronic components, or circuitry commonly used to implement such components.
[0032] FIG. 1 illustrates an exemplary block diagram representation of a network architecture implementing a proposed system for enhancing, augmenting and extending an extended reality scene, according to embodiments of the present disclosure.
[0033] FIG. 2A illustrates an exemplary detailed block diagram representation of the proposed emulating wearable device, according to embodiments of the present disclosure.
[0034] FIG. 2B illustrates an exemplary detailed block diagram representation of the proposed XR scene slider, according to embodiments of the present disclosure.
[0035] FIG. 3 illustrates a flow chart depicting a method of enhancing, augmenting and extending an extended reality scene, according to embodiments of the present disclosure.
[0036] FIG. 4 illustrates a flow chart depicting an extraction phase in a computing environment, according to embodiments of the present disclosure.
[0037] FIG. 5 illustrates a flow chart depicting an Object Studio in a computing environment, according to embodiments of the present disclosure.
[0038] FIG. 6 illustrates a flow chart depicting an exemplary scenario of the inventive idea, according to embodiments of the present disclosure.
[0039] FIG. 7 illustrates a hardware platform for the implementation of the disclosed system according to embodiments of the present disclosure.
[0040] The foregoing shall be more apparent from the following more detailed description of the invention.

DETAILED DESCRIPTION OF INVENTION
[0041] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.
[0042] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that, various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the invention as set forth.
[0043] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.
[0044] Also, it is noted that individual embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
[0045] The word “exemplary” and/or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and/or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive—in a manner similar to the term “comprising” as an open transition word—without precluding any additional or other elements.
[0046] As used herein, "connect", "configure", "couple" and its cognate terms, such as "connects", "connected", "configured", and "coupled" may include a physical connection (such as a wired/wireless connection), a logical connection (such as through logical gates of semiconducting device), other suitable connections, or a combination of such connections, as may be obvious to a skilled person.
[0047] As used herein, "send", "transfer", "transmit", and their cognate terms like "sending", "sent", "transferring", "transmitting", "transferred", "transmitted", etc. include sending or transporting data or information from one unit or component to another unit or component, wherein the content may or may not be modified before or after sending, transferring, transmitting.
[0048] Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[0049] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.
[0050] Various embodiments of the present disclosure provide a system and a method for enhancing, augmenting and extending an extended reality scene. The present disclosure provides a system and a method that can enable bidirectional interaction and further maintain consistency with the real environment, virtual reconstructions of physical scenes are to be segmented semantically and adapted dynamically. Furthermore, the present disclosure provides for a scalable interaction technique for selection, manipulation, and navigation of 3D Objects and 2D annotations in a 3D space are needed to let users intuitively switch between one or more handheld and head-mounted displays. The present disclosure further provides an interaction technique between a user and an XR scene that can aid varying degree of immersivity.
[0051] FIG. 1 illustrates an exemplary block diagram representation of a network architecture 100 implementing a proposed system 110 for enhancing, augmenting and extending an extended reality scene, according to embodiments of the present disclosure. The network architecture may include the system 110 (also referred to as the extended reality (XR) slider 110 hereinafter), an emulating wearable device 108, and a centralized server 118. The XR scene slider 110 may be connected to the centralized server 118 via a communication network 106.
[0052] The centralized server 118 may include, but is not limited to, a stand-alone server, a remote server, a cloud computing server, a dedicated server, a rack server, a server blade, a server rack, a bank of servers, a server farm, hardware supporting a part of a cloud service or system, a home server, hardware running a virtualized server, one or more processors executing code to function as a server, one or more machines performing server-side functionality as described herein, at least a portion of any of the above, some combination thereof, and the like. The communication network 106 may be a wired communication network or a wireless communication network. The wireless communication network may be any wireless communication network capable of transferring data between entities of that network such as, but is not limited to, a carrier network including a circuit-switched network, a public switched network, a Content Delivery Network (CDN) network, a Long-Term Evolution (LTE) network, a New Radio (NR), a Global System for Mobile Communications (GSM) network and a Universal Mobile Telecommunications System (UMTS) network, an Internet, intranets, Local Area Networks (LANs), Wide Area Networks (WANs), mobile communication networks, combinations thereof, and the like.
[0053] The XR scene slider 110 may be implemented by way of a single device or a combination of multiple devices that may be operatively connected or networked together. For example, the XR scene slider 110 may be implemented by way of a standalone device such as the centralized server 118, and the like, and may be communicatively coupled to the emulating wearable device 108. In another example, the XR scene slider 110 may be implemented in/ associated with the emulating wearable device 108. In yet another example, the XR scene slider 110 may be a navigable element designed as a part of the emulating wearable device 108. For example, the XR scene Slider 110 may be a control element that may be built as integral to the XR wearable device 108, that can provide an almost life-like reality where one not only gets to ‘see’ but also ‘be’. The XR scene slider 110 may be a visual slider represented as part of the UI/X field of view of the XR wearable device 108 and that can be bi-directionally maneuvered through a physical slidable mini lever (more as a protuberance) designed as part of the XR wearable device’s physical form factor.
[0054] In an embodiment, the emulating wearable device 108 (also referred to as the XR wearable device 108 herein) may be associated with one or more user 102-1, 102-2, …..., 102-N (individually referred to as the user 102, and collectively referred to as the users 102). In an example, the emulating wearable device 108 may be configured to be worn by the users 102. The emulating wearable device 108 for example can be goggles, helmet, cap and the like. In another embodiment, the emulating wearable device 108 may be at least a fully untethered six-degree-of-freedom (6DoF) headset and controller. The emulating wearable device 108 may be operatively coupled to one or more depth camera sensors 104 to capture one or more attributes of a surrounding scene of the user 102. The one or more depth camera sensors may further include head-motion-tracking sensors, which may include devices such as gyroscopes, accelerometers, magnetometers or structured light systems. The emulating wearable device 108 may be further equipped with stereoscopic head-mounted display that provides separate images for each eye, stereo sound, and the like. In an embodiment, the one or more depth camera sensors (104) may be further operatively coupled to an artificial intelligence (AI) engine 220 (Ref. FIG. 2B). Additionally, the AI engine 220 may link the XR scene Slider 110 to the surrounding environment (also referred to as the free environment) in the XR wearable device 108 and the XR Scene experienced by the user 102. Furthermore, the AI Engine 220 may be embedded in the RAM of the emulating wearable device 108.
[0055] In an exemplary embodiment, the AI engine may be integrated inside the emulating wearable device 108 and may be configured to generate one or more intensive visuals from the one or more attributes of a surrounding scene of the user.
[0056] The users 102 may be a user of, but are not limited to, an electronic commerce (e-commerce) platform, a hyperlocal platform, a super-mart platform, a media platform, a service providing platform, a social networking platform, a messaging platform, a bot processing platform, a virtual assistance platform, an Artificial Intelligence (AI) based platform, and the like. In some instances, the user 102 may include an entity/administrator.
[0057] The XR scene slider 110 may be implemented in hardware or a suitable combination of hardware and software. The XR scene slider 110 or the centralized server 118 may be associated with entities (not shown). The entities may include, but are not limited to, an e-commerce company, a company, an outlet, a manufacturing unit, an enterprise, a facility, an organization, an educational institution, a secured facility, and the like.
[0058] Further, the XR scene slider 110 may include one or more processors 112, an Input/Output (I/O) interface 114, and a memory 116. The Input/Output (I/O) interface 114 of the system 110 may be used to receive inputs, from the emulating wearable device 108 associated with one or more users 102 (collectively referred as users 102 and individually referred as user 102).
[0059] Further, the XR scene slider 110 may also include other units such as a display unit, an input unit, an output unit, and the like, however the same are not shown in FIG. 1, for the purpose of clarity. Also, in FIG. 1 only a few units are shown, however, the XR scene slider 110 or the network architecture 100 may include multiple such units or the XR scene slider 110/ network architecture 100 may include any such numbers of the units, obvious to a person skilled in the art or as required to implement the features of the present disclosure. The XR scene slider 110 may be a hardware device including the processor 112 executing machine-readable program instructions to enhancing, augmenting and extending an extended reality scene.
[0060] Execution of the machine-readable program instructions by the one or more processors 112 may enable the XT scene slider 110 for enhancing, augmenting and extending an extended reality scene. The “hardware” may comprise a combination of discrete components, an integrated circuit, an application-specific integrated circuit, a field-programmable gate array, a digital signal processor, or other suitable hardware. The “software” may comprise one or more objects, agents, threads, lines of code, subroutines, separate software applications, two or more lines of code, or other suitable software structures operating in one or more software applications or on one or more processors. The one or more processors 112 may include, for example, but are not limited to, microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuits, and any devices that manipulate data or signals based on operational instructions, and the like. Among other capabilities, the one or more processors 112 may fetch and execute computer-readable instructions in the memory 116 operationally coupled with the system 110 for performing tasks such as data processing, input/output processing, and/or any other functions. Any reference to a task in the present disclosure may refer to an operation being or that may be performed on data.
[0061] In the example that follows, assume that a user 102 of the system 110 desires to improve/add additional features for enhancing, augmenting and extending an extended reality scene. In this instance, the user 102 may include an administrator of a website, an administrator of an e-commerce site, an administrator of a social media site, an administrator of an e-commerce application/ social media application/other applications, an administrator of media content (e.g., television content, video-on-demand content, online video content, graphical content, image content, augmented/virtual reality content, metaverse content), among other examples, and the like. The system 110 when associated with the emulating wearable device 108 or the centralized server 118 may include, but is not limited to, a touch panel, a soft keypad, a hard keypad (including buttons), and the like.
[0062] In an embodiment the one or more processors 112 may cause the XR scene slider 110 to receive the one or more intensive visuals from the AI engine 220 associated with the emulating wearable device 108 and extract a first set of attributes from the one or more intensive visuals received. The first set of attributes may pertain to one or more object boundaries, location and geometry of the one or more objects with respect to each other, annotation features, filters, colors, hues, lighting, shadow effects and motion tracker outputs obtained from the one or more dept camera sensors. The XR scene slider 110 may further be configured to map the first set of attributes to one or more scenes based on a predefined set of instructions. In an exemplary embodiment, the predefined set of instructions may pertain to techniques based on Generative Adversarial Networks (GAN).
[0063] Thus, by using GAN, the XR scene slider 110 may further be configured to compose one or more XR scenes based on the mapping of the first set of attributes to the one or more scenes. The one or more XR scenes may be composed based on a plurality of immersion grades pertaining to states of immersion the user is taken through in the one or more XR scenes.
[0064] In an embodiment, the XR Scene Slider 110 may be configured to maneuver in a generated XR Field of View of the user or through a hardware such as a mini protuberance provided as a part of the emulating wearable device’s physical form factor. In an exemplary embodiment, the XR scene slider 110 can move forward or backward based on waxing or waning of the XR scene correspondingly.
[0065] In another embodiment, the XR Scene Slider 110 may be further configured to decompose one or more XR scenes based on the mapping of the first set of attributes to the one or more scenes. The one or more XR scenes may be decomposed based on a plurality of immersion grades pertaining to states of immersion the user is taken through in the one or more XR scenes.
[0066] In yet another embodiment, the plurality of immersion grades may include at least five immersion states such as essential, enhanced, elaborate, enriched and epitome states of immersion that the user is taken through progressively.
[0067] In an exemplary embodiment, the essential state, the XR scene may be composited with most minimal and essential overlay of the one or objects obtained from the surrounding scene, and the enhance state may be an improved variance of the XR scene factoring a predefine additional quantum of digital objects. Additionally, the elaborate state may be an advanced version of the XR scene that has the maximum quantum of digital objects overlay generated with the user 102 as a central point of foci for a more complete immersive experiences with digital objects spanning 2D objects, 3D objects and text overlays and in the enriched state, the degree of immersion may be increased by a predefined value that involves both video and auditory faculties of the user by blending video and spatial audio. Further, the Epitome state may include the highest degree of immersion that involves a plurality of faculties of the one more objects spanning video, text, spatial audio, haptics and associated sensor stimulants centric technologies.
[0068] In yet another embodiment, the XR Scene Slider 110 may be further configured to complete one or more incomplete surrounding scene using the creative generation capabilities of GANs, enable lighting and reflections for texturing of an XR scene, superimpose dynamic information on the XR scene related to the XR scene elements and generate meta data on the XR scene based on real-time user and environment interactions.
[0069] In an embodiment, the emulating wearable device 108 may include one or more processors 202 coupled with a memory 204, wherein the memory may store instructions which when executed by the one or more processors 202. FIG. 2A with reference to FIG. 1, illustrates an exemplary representation of the emulating wearable device 108 for generating one or more intensive visuals from the one or more attributes of a surrounding scene of the user 102, in accordance with an embodiment of the present disclosure. In an aspect, the emulating wearable device 108 may comprise one or more processor(s) 202. The one or more processor(s) 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that process data based on operational instructions. Among other capabilities, the one or more processor(s) 202 may be configured to fetch and execute computer-readable instructions stored in a memory 204 of the emulating wearable device 108. The memory 204 may be configured to store one or more computer-readable instructions or routines in a non-transitory computer readable storage medium, which may be fetched and executed to create or share data packets over a network service. The memory 204 may comprise any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as EPROM, flash memory, and the like.
[0070] In an embodiment, the emulating wearable device 108 may include an interface(s) 206. The interface(s) 206 may comprise a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, and the like. The interface(s) 206 may facilitate communication of the emulating wearable device 108. The interface(s) 206 may also provide a communication pathway for one or more components of the emulating wearable device 108. Examples of such components include, but are not limited to, processing engine(s) 208 and a database 210.
[0071] The processing engine(s) 208 may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) 208. In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) 208 may be processor executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine(s) 208 may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) 208. In such examples, the emulating wearable device 108 may comprise the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the emulating wearable device 108 and the processing resource. In other examples, the processing engine(s) (208) may be implemented by electronic circuitry.
[0072] The processing engine 208 may include one or more engines selected from any of a data acquisition module 212, an Image Extractor 214, an Object Extractor 216, a Stipulator 218, an AI engine 220, an Object Studio 222, an Object Positioning module 224 and a Scene Generator 226.
[0073] In an embodiment, the data acquisition module 212 may be configured to acquire one or more attributes of a surrounding scene of the user 102.
[0074] In an exemplary embodiment, the image extractor 214 may extract an image from the surrounding scene of the user. The object extractor 216 may extract one or more objects from the image. The stipulator 218 may process the one or more objects extracted and the object studio 222 may create a new set of objects based on the processed one or more objects. The object positioning module 224 may position the new set of objects created; and the scene generator 226 with the AI engine 220 may generate one or more intensive visuals from the one or more attributes of a surrounding scene of the user.
[0075] In an embodiment, the AI engine 220 may further be configured to correlate the user’s need to vary an XR experience to seamlessly combine real objects, virtual objects, annotation items and other elements that make the one or more intensive visuals.
[0076] Additionally, the AI engine 220 may further titrate the growth or depletion of the XR Scene that the AI engine 220 is compositing based on the forward or backward movement of the XR scene slider 110. In yet another embodiment, the AI engine 220 may use convolutional neural network to generate an image with accurate retinal blur as soon as the eye of the user in the emulating wearable device moves over a scene and minimize one or more spatial dimensions of the image while fully preserving visual information. The AI Engine 220 makes use of cutting-edge computer vision (CV) techniques as well as visual-inertial simultaneous localization and mapping (SLAM).
[0077] The AI engine 220 can help solve the challenges of rendering highly compute-intensive visuals in XR. The AI engine 220 can enhance the XR images by help of its deep learning capabilities. This is accomplished through the use of a revolutionary end-to-end convolutional neural network that generates an image with accurate retinal blur as soon as the eye moves over a scene. To minimize the spatial dimensions of the input while fully preserving visual information, the network contains new volume-preserving interleaving layers. The network's convolutional layers then operate on the same, lower spatial resolution, with a substantially shorter runtime. The AI engine 220 may be equipped with a specialized hardware architecture and advanced computer vision algorithms, such as visual-inertial mapping, place identification, and geometry reconstruction, to determine the location of things in respect to other items inside a given region.
[0078] In an exemplary embodiment, the other engines 228 may include an segmentation module, a detection processing module, an alert generation module.
[0079] FIG. 2B illustrates an exemplary detailed block diagram representation of the proposed system/ XR scene slider 110, according to embodiments of the present disclosure. The system 110 may include the processor 112, the Input/Output (I/O) interface 114, and the memory 116. In some implementations, the system 110 may include data 252, and modules 254. As an example, the data 252 may be stored in the memory 116 configured in the system 110 as shown in FIG. 2B.
[0080] In an embodiment, the data 252 may include image data 256, object data 258, background data 260, XR scene data 262, and other data 264. In an embodiment, the data 202 may be stored in the memory 116 in the form of various data structures. Additionally, the data 202 can be organized using data models, such as relational or hierarchical data models. The other data 264 may store data, including temporary data and temporary files, generated by the modules 254 for performing the various functions of the system 110.
[0081] In an embodiment, the modules 254, may include a receiving module 272, a Generative adversarial networks (GAN) module 274, and other modules 276.
[0082] In an embodiment, the data 252 stored in the memory 116 may be processed by the modules 254 of the system 110. The modules 254 may be stored within the memory 116. In an example, the modules 254 communicatively coupled to the processor 112 configured in the system 110, may also be present outside the memory 116, as shown in FIG. 2B, and implemented as hardware. As used herein, the term modules refer to an Application-Specific Integrated Circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and memory that execute one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality.
[0083] In an embodiment, the receiving module 232 may receive the one or more intensive visuals from the AI engine 220 associated with the emulating wearable device 108 and extract a first set of attributes from the one or more intensive visuals received. The first set of attributes may pertain to one or more object boundaries, location and geometry of the one or more objects with respect to each other, annotation features, filters, colors, hues, lighting, shadow effects and motion tracker outputs obtained from the one or more dept camera sensors. The GAN module 274 may further be configured to map the first set of attributes to one or more scenes based on a predefined set of instructions.
[0084] For example, GANs are based on unsupervised learning which does not need labeled data. GAN can enable high-quality natural images that develop gradually to generate more and more realistic looking data by coupling with an adversarial network. The range of capabilities for GANs include - the possibility of generating very high-quality synthetic data, ability to enhance pixels in photos, generate images from the input text, convert images from one domain to another, change the appearance of the face image and the like. In a scenario when there is not enough data for a problem being worked upon, adversarial networks can “generate” more data, instead of resorting to ways like data augmentation. GANs have also proved useful in many tasks which intrinsically require the realistic generation of samples from some distribution. When we have several tasks to complete, a single input may have a close relation to many alternative right outputs, each of which is acceptable. Most Generative models, and GANs, in particular, allow machine learning to operate with multi-modal outputs. GANs can further show a form of pseudo-imagination depending on the task they are performing. GANs still need a wealth of training datasets to get started. GANs cannot invent entirely new things. GANs can only be expected to combine what they already know in new ways. Thus, GAN serve to providing scalable learning environments that do not require operating and storing virtual 3D data for entire environments. GANs can also be used for improving Extended Reality (XR) scenes where incomplete environment maps can be completed using the creative generation capabilities of GANs through learning the statistical structure of the world. Other XR-related use cases of GANs involve environment texturing such as enabling lighting and reflections. GANs with XR can enable to superimpose dynamic information related to the XR scene elements and generate meta data on their environments based on real-time user and environment interactions. GANs can also augment the XR scene with appropriate digital objects that can step-wise progressively engross the user into varying degree of immersive-ness as the user chooses to experience.
[0085] Thus, by using the GAN module 274, the XR scene slider 110 may further be configured to compose one or more XR scenes based on the mapping of the first set of attributes to the one or more scenes, wherein the one or more XR scenes are composed based on a plurality of immersion grades pertaining to states of immersion the user is taken through in the one or more XR scenes.
[0086] FIG. 3 illustrates a flow chart depicting a method 300 of enhancing, augmenting and extending an extended reality scene, according to embodiments of the present disclosure.
[0087] At block 302, the method 300 includes, receiving, by one or more processors 112, the one or more intensive visuals from the AI engine 220 associated with the emulating wearable device 108.
[0088] At block 304, the method 300 includes extracting, by the one or more processors 112, a first set of attributes from the one or more intensive visuals received, said first set of attributes pertaining to one or more object boundaries, location and geometry of the one or more objects with respect to each other, annotation features, filters, colors, hues, lighting, shadow effects and motion tracker outputs obtained from the one or more dept camera sensors 104.
[0089] At block 306, the method 300 includes mapping, by the one or more processors 112, the first set of attributes to one or more scenes based on a predefined set of instructions pertaining to techniques based on Generative Adversarial Networks (GAN).
[0090] At block 308, the method 300 includes composing, by the one or more processors 112, one or more XR scenes based on the mapping of the first set of attributes to the one or more scenes. The one or more XR scenes are composed based on a plurality of immersion grades pertaining to states of immersion the user is taken through in the one or more XR scenes.
[0091] The order in which the method 300 are described is not intended to be construed as a limitation, and any number of the described method blocks may be combined or otherwise performed in any order to implement the method 300 or an alternate method. Additionally, individual blocks may be deleted from the method 300 without departing from the spirit and scope of the present disclosure described herein. Furthermore, the method 300 may be implemented in any suitable hardware, software, firmware, or a combination thereof, that exists in the related art or that is later developed. The method 300 describe, without limitation, the implementation of the system 110. A person of skill in the art will understand that method 300 may be modified appropriately for implementation in various manners without departing from the scope and spirit of the disclosure.
[0092] FIG. 4 illustrates a flow chart depicting an extraction phase in the emulating wearable device 108, according to embodiments of the present disclosure. With reference to FIG. 2A, the extraction phrase may include an image extractor 214 and an object extractor 216 extracting one or more images and one or more objects respectively from a free environment 402. The extraction of the one or more images and one or more objects may undergo further processing based on a rule engine 404. The rule engine 404 may further include modules such as geometry analysis 406, texture generation 408, modelling cues analysis 410, animation analysis 412, space warps analysis 414, physical properties analysis 416, interaction analysis 418 and inter object connect of the one or more images and one or more objects.
[0093] FIG. 5 illustrates a flow chart depicting an Object Studio in a computing environment, according to embodiments of the present disclosure. As illustrated in FIG. 5, the extracted the one or more images and the one or more objects (Ref. Fig. 4) are sent to the object studio 222. The object studio 222 may create a new set of objects based on the processed one or more objects through an object factory 502 that may include modules such as 3D objects 504, 2D assets 506, multimedia files 508, user files 510, embellishment library 512 and the like. The one or more new objects may then undergo mesh generation 514, global illumination 516, animation selector 518, inter object connect 520, and spatial audio rendition 522. The output is then compared with Threshold 1 524, Threshold 2 526, Threshold 3 528, Threshold 4 530, Threshold 5 532 and the like and passed on to the XR scene slider 536 and then finally to a VR or an AR headset 538.
[0094] FIG. 6 illustrates a flow chart depicting an exemplary scenario of the inventive idea, according to embodiments of the present disclosure. The exemplary scenario illustrates a flow chart how a smartwatch or wearable device 602 is associated with a user 604 to collect one or more signals 606 associated with the user 604. The one or more signals 606 may include pulse, oxygen levels, stress levels, heart rate, calorie count, BMI, ECG reading and the like. The one or more signals is similar to the free environment of the user in the proposed system 110. The user details 604 and the one or more signals 606 may be processed by a method, an application interface, a system to obtain a digital identity 608 comprising dynamic Non-Fungible Token (NFTs), metaverse avatar similar to the XR scenes generated by the proposed system 110. A dynamic engine 610 processes the digital identity by working on one or more changes and suggestions 612 pertaining to visual metadata of NFT, visual appearance of the avatar, auto additions to the user’s grocery cart, metaverse environment weather and more, one or more rewards obtained by the user 604 from one or more platforms based on the user’s physical health. The Dynamic engine 610 then may provide an effective real world health score 614 that has a consideration in the user’s metaverse and the life of the user as well.
[0095] FIG. 7 illustrates a hardware platform 700 for implementation of the disclosed system 110, according to an example embodiment of the present disclosure. For the sake of brevity, construction, and operational features of the system 110 which are explained in detail above are not explained in detail herein. Particularly, computing machines such as but not limited to internal/external server clusters, quantum computers, desktops, laptops, smartphones, tablets, and wearables which may be used to execute the system 110 or may include the structure of the hardware platform 700. As illustrated, the hardware platform 700 may include additional components not shown, and that some of the components described may be removed and/or modified. For example, a computer system with multiple GPUs may be located on external-cloud platforms including Amazon® Web Services, or internal corporate cloud computing clusters, or organizational computing resources, etc.
[0096] The hardware platform 700 may be a computer system such as the system 110 that may be used with the embodiments described herein. The computer system may represent a computational platform that includes components that may be in a server or another computer system. The computer system may execute, by the processor 705 (e.g., a single or multiple processors) or other hardware processing circuit, the methods, functions, and other processes described herein. These methods, functions, and other processes may be embodied as machine-readable instructions stored on a computer-readable medium, which may be non-transitory, such as hardware storage devices (e.g., RAM (random access memory), ROM (read-only memory), EPROM (erasable, programmable ROM), EEPROM (electrically erasable, programmable ROM), hard drives, and flash memory). The computer system may include the processor 705 that executes software instructions or code stored on a non-transitory computer-readable storage medium 710 to perform methods of the present disclosure. The software code includes, for example, instructions to gather data and documents and analyze documents. In an example, the modules 207, may be software codes or components performing these steps.
[0097] The instructions on the computer-readable storage medium 710 are read and stored the instructions in storage 715 or in random access memory (RAM). The storage 715 may provide a space for keeping static data where at least some instructions could be stored for later execution. The stored instructions may be further compiled to generate other representations of the instructions and dynamically stored in the RAM such as RAM 720. The processor 705 may read instructions from the RAM 720 and perform actions as instructed.
[0098] The computer system may further include the output device 725 to provide at least some of the results of the execution as output including, but not limited to, visual information to users, such as external agents. The output device 725 may include a display on computing devices and virtual reality glasses. For example, the display may be a mobile phone screen or a laptop screen. GUIs and/or text may be presented as an output on the display screen. The computer system may further include an input device 730 to provide a user or another device with mechanisms for entering data and/or otherwise interacting with the computer system. The input device 730 may include, for example, a keyboard, a keypad, a mouse, or a touchscreen. Each of these output devices 725 and input device 730 may be joined by one or more additional peripherals. For example, the output device 725 may be used to display the results such as bot responses by the executable chatbot.
[0099] A network communicator 735 may be provided to connect the computer system to a network and in turn to other devices connected to the network including other clients, servers, data stores, and interfaces, for instance. A network communicator 735 may include, for example, a network adapter such as a LAN adapter or a wireless adapter. The computer system may include a data sources interface 740 to access the data source 745. The data source 745 may be an information resource. As an example, a database of exceptions and rules may be provided as the data source 745. Moreover, knowledge repositories and curated data may be other examples of the data source 745.
[00100] While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the invention and not as a limitation.

ADVANTAGES OF THE PRESENT DISCLOSURE
[00101] The present disclosure provides a system and a method for enhancing, augmenting and extending an extended reality scene.
[00102] The present disclosure provides a system and a method for achieving the optimum balancing of the three variables, balancing interaction, engagement, and content ascertaining depth of immersivity experienced.
[00103] The present disclosure provides an approach to image synthesis for rendering the relevant XR scenes / experiences to the users.
[00104] The present disclosure provides a tool of interaction between the immersive environment, the user and the AI / GAN based engine that provides the XR Construct.
[00105] The present disclosure provides a system and a method to vary the degree of immersion of the viewer with the XR Scene.
[00106] The present disclosure uses GAN to do image synthesis by learning from the limited data and by using synthetic data for the relevant scene generation.
[00107] The present disclosure provides a bidirectional interaction and maintain consistency with the real environment, virtual reconstructions of physical scenes are to be segmented semantically and adapted dynamically.
[00108] The present disclosure provides a scalable interaction technique for selection, manipulation, and navigation of 3D Objects and 2D annotations in a 3D space to let users intuitively switch between handheld and head-mounted displays.
[00109] The present disclosure provides an interaction technique through a tool for interface between the user and the XR scene that can aid varying degree of immersivity.

, Claims:1. A system for generating and enhancing an extended reality (XR) scene in a computing environment, the system comprising:
an emulating wearable device 108, said emulating wearable device 108 configured to be worn by a user 102, wherein said emulating wearable device 108 comprises one or more depth camera sensors 104to capture one or more attributes of a surrounding scene of the user 102, wherein the one or more depth camera sensors 104 are further operatively coupled to an artificial intelligence (AI) engine 220, the AI engine 220 integrated inside the emulating wearable device 108, the AI engine 220 further configured to:
generate one or more intensive visuals from the one or more attributes of a surrounding scene of the user 102; and,
an XR scene slider 110 operatively coupled to the emulating wearable device 108, said XR scene slider 110 comprising one or more processors 112 coupled with a memory 116, the memory 116 storing instructions which when executed by the one or more processors 112, causes the XR scene slider 110 to:
receive the one or more intensive visuals from the AI engine 220 associated with the emulating wearable device 108;
extract a first set of attributes from the one or more intensive visuals received, said first set of attributes pertaining to one or more object boundaries, location and geometry of the one or more objects with respect to each other, annotation features, filters, colors, hues, lighting, shadow effects and motion tracker outputs obtained from the one or more dept camera sensors 104;
map the first set of attributes to one or more scenes based on a predefined set of instructions, wherein the predefined set of instructions pertain to techniques based on Generative Adversarial Networks (GAN); and

compose one or more XR scenes based on the mapping of the first set of attributes to the one or more scenes, wherein the one or more XR scenes are composed based on a plurality of immersion grades, said plurality of immersion grades pertain to states of immersion the user 102 is taken through in the one or more XR scenes.
2. The system as claimed in claim 1, wherein the emulating wearable device 108 further comprises at least a six-degree-of-freedom (6DoF) headset and a controller, wherein the headset and the controller comprise the one or more depth cameras, one or more sensor suites, one or more motion controllers and human interfaces.
3. The system as claimed in claim 1, wherein to generate the one or more intensive visuals, the AI engine 220 integrated in the emulating wearable device 108 further comprises an Image Extractor 214, an Object Extractor 216, a Stipulator 218, an Object Studio 222, an Object Positioning module 224 and a Scene Generator 226.
4. The system as claimed in claim 3, wherein to generate the one or more intensive visuals, the AI engine 220 integrated in the emulating wearable device 108 is further configured to:
extract, using the image extractor 214, an image from the surrounding scene of the user 102 ;
extract, using the object extractor 216, one or more objects from the image;
process, using the stipulator 218, the one or more objects extracted;
create, using the object studio 222, a new set of objects based on the processed one or more objects;
position, using the object positioning module 224, the new set of objects created; and,
generate, by the scene generator 226, the one or more intensive visuals.
5. The system as claimed in claim 4, wherein the AI engine 220 is further configured to:
correlate the user 102 ’s need to vary an XR experience to seamlessly combine real objects, virtual objects, annotation items and other elements that make the one or more intensive visuals.
6. The system as claimed in claim 1, wherein the XR scene slider 110 is a navigable element designed as a part of the emulating wearable device 108, wherein the XR scene slider 110 is configured to maneuver in a generated XR Field of View of the user 102 or through a hardware such as a mini protuberance provided as a part of the emulating wearable device’s physical form factor.
7. The system as claimed in claim 6, wherein the XR scene slider 110 moves forward or backward based on waxing or waning of the XR scene correspondingly.
8. The system as claimed in claim 7, wherein the AI engine 220 titrates the growth or depletion of the XR Scene that the AI engine 220 is compositing based on the forward or backward movement of the XR scene slider.
9. The system as claimed in claim 8, wherein the AI engine 220 is further configured to:
uses convolutional neural network to generate an image with accurate retinal blur as soon as the eye of the user 102 in the emulating wearable device 108moves over a scene;
minimize one or more spatial dimensions of the image while fully preserving visual information.
10. The system as claimed in claim 7, wherein the XR scene slider 110 is further configured to decompose one or more XR scenes based on the mapping of the first set of attributes to the one or more scenes, wherein the one or more XR scenes are decomposed based on a plurality of immersion grades, said plurality of immersion grades pertain to states of immersion the user 102 is taken through in the one or more XR scenes.
11. The system as claimed in claim 10, wherein the plurality of immersion grades comprises at least five immersion states such as essential, enhanced, elaborate, enriched and epitome states of immersion that the user 102 is taken through progressively.
12. The system as claimed in claim 11, wherein in the essential state, the XR scene is composited with most minimal and essential overlay of the one or objects obtained from the surrounding scene, wherein the enhance state is an improved variance of the XR scene factoring a predefine additional quantum of digital objects, wherein the elaborate state is an advanced version of the XR scene that has the maximum quantum of digital objects overlay generated with the User 102 as a central point of foci for a more complete immersive experiences with digital objects spanning 2D objects, 3D objects and text overlays, wherein in the enriched state the degree of immersion is increased by a predefined value that involves both video and auditory faculties of the user 102 by blending video and spatial audio, and wherein the Epitome state has the highest degree of immersion that involves a plurality of faculties of the one more objects spanning video, text, spatial audio, haptics and associated sensor stimulants centric technologies.
13. The system as claimed in claim 1, wherein the XR scene slider 110 is configured to:
complete one or more incomplete surrounding scene using the creative generation capabilities of GANs;
enable lighting and reflections for texturing of an XR scene;
superimpose dynamic information on the XR scene related to the XR scene elements;
and generate meta data on the XR scene based on real-time user 102 and environment interactions.
14. A method for generating and enhancing an extended reality (XR) scene in a computing environment, the method comprising:
receiving, by one or more processors 112, the one or more intensive visuals from an artificial intelligence (AI) engine 220 associated with an emulating wearable device 108, wherein the one or more processors 112 are associated with an XR scene slider 110, wherein the XR scene slider 110 is operatively coupled to the emulating wearable device 108, said emulating wearable device 108 configured to be worn by a user 102 , wherein said emulating wearable device 108 comprises one or more depth camera sensors 104 to capture one or more attributes of a surrounding scene of the user 102 , wherein the one or more camera depth sensors 104 are further operatively coupled to the AI engine 220 integrated inside the emulating wearable device 108, wherein the AI engine 220, generates one or more intensive visuals from the one or more attributes of a surrounding scene of the user 102 ;
extracting, by the one or more processors 112, a first set of attributes from the one or more intensive visuals received, said first set of attributes pertaining to one or more object boundaries, location and geometry of the one or more objects with respect to each other, annotation features, filters, colors, hues, lighting, shadow effects and motion tracker outputs obtained from the one or more dept camera sensors;
mapping, by the one or more processors 112, the first set of attributes to one or more scenes based on a predefined set of instructions, wherein the predefined set of instructions pertain to techniques based on Generative Adversarial Networks (GAN); and
composing, by the one or more processors 112, one or more XR scenes based on the mapping of the first set of attributes to the one or more scenes, wherein the one or more XR scenes are composed based on a plurality of immersion grades, said plurality of immersion grades pertain to states of immersion the user 102 is taken through in the one or more XR scenes.
15. The method as claimed in claim 14, wherein to generate the one or more intensive visuals, the AI engine 220 integrated in the emulating wearable device 108 is further configured to:
extracting, using an image extractor 214 associated with the AI engine 220, an image from the surrounding scene of the user 102;
extracting, using an object extractor 216 associated with the AI engine 220, one or more objects from the image;
processing, using a stipulator 218 associated with the AI engine 220, the one or more objects extracted;
creating, using an object studio 222 associated with the AI engine 220, a new set of objects based on the processed one or more objects;
positioning, using an object positioning module 224 associated with the AI engine 220, the new set of objects created;
generating, by a scene generator 226 associated with the AI engine 220, the one or more intensive visuals; and,
correlating, by the AI engine 220, the user 102 ’s need to vary an XR experience to seamlessly combine real objects, virtual objects, annotation items and other elements that make the one or more intensive visuals.
16. The method as claimed in claim 14, wherein the method comprises the step of
decomposing, by the XR Scene, one or more XR scenes based on the mapping of the first set of attributes to the one or more scenes, wherein the one or more XR scenes are decomposed based on a plurality of immersion grades, said plurality of immersion grades pertain to states of immersion the user 102 is taken through in the one or more XR scenes, wherein the plurality of immersion grades comprises at least five immersion states such as essential, enhanced, elaborate, enriched and epitome states of immersion that the user 102 is taken through progressively.
17. A device for generating and enhancing an extended reality (XR) scene in a computing environment, the device comprising:
an emulating wearable device 108, said emulating wearable device 108 configured to be worn by a user 102 , wherein said emulating wearable device 108 comprises one or more depth camera sensors 104 to capture one or more attributes of a surrounding scene of the user 102 , wherein the one or more camera depth sensors 104 are further operatively coupled to an artificial intelligence (AI) engine, the AI engine 220 integrated inside the emulating wearable device, the AI engine 220 further configured to:
generate one or more intensive visuals from the one or more attributes of a surrounding scene of the user 102 ; and,
an XR scene slider 110 operatively coupled to the emulating wearable device 108, said XR scene slider 110 comprising one or more processors 112 coupled with a memory 116, the memory 116 storing instructions which when executed by the one or more processors 112, causes the XR scene slider 110 to:
receive the one or more intensive visuals from the AI engine 220 associated with the emulating wearable device 108;
extract a first set of attributes from the one or more intensive visuals received, said first set of attributes pertaining to one or more object boundaries, location and geometry of the one or more objects with respect to each other, annotation features, filters, colors, hues, lighting, shadow effects and motion tracker outputs obtained from the one or more dept camera sensors;
map the first set of attributes to one or more scenes based on a predefined set of instructions, wherein the predefined set of instructions pertain to techniques based on Generative Adversarial Networks (GAN); and
compose one or more XR scenes based on the mapping of the first set of attributes to the one or more scenes, wherein the one or more XR scenes are composed based on a plurality of immersion grades, said plurality of immersion grades pertain to states of immersion the user 102 is taken through in the one or more XR scenes.
18. The device as claimed in claim 17, wherein the emulating wearable device 108 further comprises at least a six-degree-of-freedom (6DoF) headset and a controller, wherein the headset and the controller comprise the one or more depth cameras, one or more sensor suites, one or more motion controllers and human interfaces.
19. The device as claimed in claim 17, wherein the XR scene slider 110 is a navigable element designed as a part of the emulating wearable device 108, wherein the XR scene slider 110 is configured to maneuver in a generated XR Field of View of the user 102 or through a hardware such as a mini protuberance provided as a part of the emulating wearable device’s 108 physical form factor.
20. The system as claimed in claim 19, wherein the XR scene slider 110 moves forward or backward based on waxing or waning of the XR scene correspondingly.