Extensive study of AI applications in Virtual Reality

Extensive study of AI applications in Virtual Reality

Maithili Badhan
Virtual Reality

Blending artificial intelligence (AI) in virtual reality (VR) is changing how we interact with the world. AI can create more realistic and immersive VR experiences by generating 3D models, textures, and scenes. It can track and interact in VR by understanding the user's movements and facial expressions. Businesses can use this technology in training, customer service, and market research. Early adopters of AI-powered VR will be well-prepared to leverage this emerging technology.

In this article, we will discuss how artificial intelligence is improving virtual reality. It will help you understand the use of this next-level technology in your business.

How AI Can Be Used In VR?

Utilizing the below-mentioned techniques within VR environments has led to substantive outcomes. These technologies collectively enhance human-computer interactions, elevate visual fidelity, and improve object recognition accuracy. The intersection of AI and VR is reshaping how we experience and interact within virtual spaces, opening doors to a new era of immersive possibilities and practical applications.

Natural Language Interaction in VR

Natural Language Processing (NLP) helps computers to understand human language. It can be integrated with VR using five components: a text parser, a rule base, an NLP to VR interface, a library of CAD (Computer Aided Design) models in ASCII formats, and a renderer. The text parser first receives the input from the user. It then breaks the input into parts and extracts relevant information from it. It would then generate a rule base, a set of rules that maintain the actions the renderer can perform. The NLP to VR interface fetches the required object from the library. It directs the extracted details and the rule base to the renderer. The renderer displays the model in the VR environment.

NLP techniques can assist voice-activated navigation, a more immersive and natural way to interact with VR environments. For example, Bark is a multilingual text-to-audio framework by Suno that can be used to generate realistic speech and sounds.

It can also be used to create more realistic and engaging NPC interactions. An NPC could respond to a user's voice commands, or it could even hold a conversation with the user. One such recent example: A Unity/Unreal player tries to convince AI NPCs that they are in stimulation and do not exist beyond it.

Also, Stanford researchers developed computer programs called Generative Agents that can simulate authentic human behavior. The model learns from real-world data and generates realistic conversations, interactions, and decisions. The agents interact with each other using natural language.

Figure From Artisana

Image and Object Recognition

Image and object detection in VR uses computer vision techniques to identify objects in the virtual world. Deep learning techniques for object detection are one-stage, two-stage or transformer-based algorithms. One-stage algorithms generate positioning coordinates and classification probabilities of objects in an image, in a single shot. Two-stage networks on the other hand use a region proposal network to propose possible regions where an object can be found, then a detection network is used to classify the objects. One-stage networks are faster than two-staged ones because of no advance region proposal generation.

YOLO series are a part of one-stages algorithms. YONO-NAS is a neural architecture search (NAS) algorithm by Deci AI. It is one of the SOTA models that surpassed previous YOLO models. It finds the optimal network architecture for object detection. It searches a large space of possible network architectures and selects the one that achieves the best performance on a given dataset. Its components are backbone, neck, head, QSP, and QSI block.

Figure from Deci AI

The backbone is responsible for extracting features from the input image. It is a convolutional neural network (CNN) that has been pre-trained on a large dataset of images. The neck connects the backbone to the head. It consists of a few convolutional layers to reduce the dimensionality of the features extracted by the backbone. The head generates the bounding boxes and class predictions for the objects in the image. It consists of a few convolutional layers to classify the objects in the image and to predict the bounding boxes for those objects. The QSP block is responsible for quantizing the features extracted by the backbone. It makes the features more efficient to process and allows YOLO-NAS to be used on devices with limited computational resources. The QSI block is responsible for dequantizing the features extracted by the QSP block. It allows YOLO-NAS to generate high-quality object detection results.

Figure From MUO

Object recognition has a major application in hand gesture recognition that allows VR systems to track the movements of the user's hands and use this information to control objects in the VR environment. There are a variety of different hand gesture recognition techniques that can be used in VR, including optical tracking, inertial tracking, and depth sensing. Once the user's hand movements have been tracked, the VR system can use this information to control objects in the VR environment, such as grabbing and moving objects, or interacting with menus and buttons.


Neural Radiance Fields (NeRFs) is a technology to create photorealistic 3D models of objects from a collection of images. It is a powerful tool to create VR experiences that are more immersive and realistic. NeRF uses a neural network to learn the relationship between the 3D position of a point in space and the color of the light reflected from that point. This relationship is called a radiance field. The neural network is trained on a dataset of images taken from different viewpoints of the object. It then generates a 3D model of the object by taking a ray from the viewer's eye and tracing it through the radiance field. Then the neural network determines the color of the light reflected from the object. It is repeated for every pixel in the image.

How Neural Radiance Fields (NeRF) and Instant Neural Graphics Primitives  work | AI Summer

NeRF model editing and saving are aspects of using NeRF for VR. NeRF model editing is the process of changing the 3D model generated by NeRF. It improves the accuracy or realism of the model, or changes the object's appearance. NeRF model saving is the process of storing the 3D model generated by NeRF. Point clouds are a good choice for VR experiences that require high performance, while meshes are a good choice for VR experiences that require high accuracy.

Recently, NVIDIA released Neuralangelo, a new AI technology that can transform any video into a highly detailed 3D environment. Neuralangelo is based on Instant NeRF, but it improves the quality of the generated models by using numerical gradients and coarse-to-fine optimization. It takes a 2D video as input and analyzes it to extract details such as depth, size, and the shapes of objects. It then uses this information to create an initial 3D model of the scene.

Gaussian Splatting

Gaussian splatting is a technique to render volumetric data. It can render a 3D scene from a set of images. The first step is to create a sparse point cloud from the images. It can estimate the position, covariance matrix, and opacity of a set of 3D Gaussians. The Gaussians are then used to render the scene.

Figure From 3D Gaussian Splatting for Real-Time Radiance Field Rendering

The volume is divided into a grid of voxels. For each voxel, the Gaussians are sampled at the voxel's center. The sampled values then create a smooth, continuous surface approximating the true volumetric data. This process repeats for all voxels in the volume. The resulting surface can then be rendered using many techniques, such as ray tracing or rasterization.

Gaussian splatting is much better than NeRF AI for rendering 3D scenes from a single image because it is more efficient, robust to noise, and flexible. Gaussian splatting is also a better choice for scenes with complex materials. It has competitive training times. This means that it can be trained quickly, even on large datasets. It can achieve high-quality results with only SfM points as input. This means that it does not require additional data, such as Multi-View Stereo (MVS) data, which can be time-consuming and expensive to collect.

The Gaussian splatting technique is a powerful tool that can render volumetric data in various applications. It is relatively simple and efficient, making it a good choice for real-time applications. It is a good choice for rendering dynamic scenes, such as those that contain moving objects or changing lighting conditions.

Stable Diffusion  

Stable diffusion is a new model used to generate high-quality images from text descriptions. The diffusion model has three main components: the image encoder, the image information creator and an autoencoder decoder. The image encoder compresses the input image into a latent representation. This latent representation is a lower-dimensional representation of the image that contains the most important information about the image. The image information creator takes the latent representation from the image encoder and generates a sequence of numbers for pixels in the image. The autoencoder decoder takes the information from the image information creator and reconstructs the original image. The autoencoder decoder is trained to minimize the difference between the reconstructed image and the original image.

To generate images,a random latent matrix is generated. Smaller the latent space faster the image generation process. The noise predictor estimates the noise in the latent matrix. The estimated noise is subtracted from the latent matrix. This process is repeated for multiple steps. With each step, some noise is removed from the latent matrix, and the given prompt is used for guidance resulting in a more accurate image that is closer to the prompt. The decoder then converts the latent matrix to the final image. 

Figure From The Illustrated Stable Diffusion

The text description guides the diffusion process so the generated image matches the description. The text prompt you provide the model gets converted into numbers relating to the individual words, called tokens. Each token gets converted to a 768-value vector known as embedding. These embeddings get processed and ready to be consumed by the noise predictor.

Stable diffusion models can be used to create realistic and immersive environments, generate interactive objects, and empower creativity in VR. It can create various VR experiences, such as virtual worlds, museum exhibits, games, and fashion shows.


ControlNet is a neural network structure to control pre-trained large diffusion models to support additional input conditions. It creates two copies of the weights. The locked copy has frozen weights and preserves the original model. The trainable copy learns to manipulate the inputs of the network to control the overall behavior of the network. It allows ControlNet to be trained on small datasets of image pairs. It can be trained on personal devices, and it can scale to large amounts of data if powerful computation clusters are available.

Figure From Cameralyze


Many text-to-3D image generation models are available today, such as Spline AI and DreamFusion. The first such model was Shap-E. It is a diffusion model created by OpenAI. It creates 3D objects using text or image input. It is trained on a conditional diffusion model and 3D asset mapping. Here are 3D images for “A penguin” and “A chair that looks like an avocado” by Shap-E:

Figure From GitHub

Shap-E comprises two models: an encoder that converts 3D assets into compact neural network codes, and a latent diffusion model that generates novel 3D assets based on images or text, needing additional steps for finalization. The models in Shap-E are trained on a variety of datasets, including a million more 3D assets and 120K captions from human annotators. It uses 60 different angles to understand how 3D things look.

It produces 3D assets compared to 2D images created by DALL-E. Shap-E achieves comparable or better CLIP R-Precision scores than optimization-based methods while being significantly faster to sample. This makes Shap-E a good choice for applications where speed is important, such as real-time 3D content generation. It can generate realistic and diverse 3D models. When taken randomly selected image-conditional samples from both Point-E and Shap-E for the same conditioning images. The samples from Shap-E are generally more realistic and diverse than the samples from Point-E.

Digital Twins and Stimulation

A digital twin is a virtual copy of a real-world object or process. Its integration with virtual reality helps users to design or redesign systems in VR environments. They can see how the object or process behaves in real-time and make changes to the design as needed. It can help to improve the design of the system and reduce the risk of problems. 

The co-stimulation workspace, a shared space where users can interact with a digital twin in virtual reality, has three main tools, a digital twin, a data server, and a VR environment. The digital twin allows users to interact with a live simulation of the object. The data server is responsible for exchanging real-time data between the digital twin and the virtual reality environment. The virtual reality environment uses the simulation data to visualize and interact with the object.

Figure From Taylor & Francis Online

The digital twin block and the data server block are connected to each other using the Functional Mock-up Interface (FMI) standard. The FMI standard is a software independent standard that allows different simulation tools to communicate with each other. The data server block and the virtual reality environment block are connected to each other using the ZMQ socket machine-to-machine communication protocol. The ZMQ socket protocol is a lightweight and efficient protocol that is well-suited for real-time data exchange.

Figure From ResearchGate

The workspace can assess the safety of the system, train operators on how to use the system, make design changes, and save time and money by avoiding the need to build physical prototypes.

How Does Generative AI Enhance VR?

With the help of above mentioned techniques, Generative AI can enhance VR by leveraging its ability to create new content, such as photorealistic virtual environments, lifelike characters, and interactive objects. It can personalize VR experiences, making them more enjoyable for users. The following will provide you an in-depth insight about it.


Perception creates realistic and immersive VR environments. AI algorithms perceive the user’s environment and actions allowing for natural and engaging interactions with the virtual world. 

For example, AI algorithms create realistic and diverse virtual environments, new hyper-real characters, creatures, and objects by training on large datasets of real-world scenes, 3-D models, textures, and animations. It can save developers time and effort, as they no longer need to manually design every aspect of the virtual world. Also, it will add rich and interactive content to VR. 

AI perception algorithms can track the user's head and eye movements, which can control the view in VR. Tracking the user's hand movements to interact with objects in VR can make the user feel like they are actually interacting with the virtual world.

AI algorithms enable procedural generation techniques in VR for dynamic and infinite content creation. Developers can create endless variations of landscapes, levels, and objects in real-time. It leads to more interactive and engaging VR experiences.


Performance is critical for VR experiences as users expect smooth and responsive visuals. AI can upscale and super-resolve VR graphics. It can generate higher-resolution images and textures from lower-resolution sources to improve the visual quality of VR experiences without increasing the computational demands.

AI can optimize rendering techniques. By dynamically adjusting settings based on the scene complexity and user interactions, AI can help to ensure that VR experiences are rendered at a high frame rate, even on low-powered devices. It can reduce the size of VR experiences by compressing data without sacrificing quality. It makes VR more accessible to users with limited bandwidth or storage space.

AI can anticipate user movements to reduce latency and improve the overall responsiveness of VR experiences. By anticipating where the user is going to look or move, AI can ensure that the VR environment is rendered correctly and in a timely manner.


AI has a major role in developing new and innovative VR content. AI can generate realistic sound effects, music, and speech in real time, matching the virtual environment and the user's actions. It contributes to a more immersive and engaging VR experience. For example, AI can generate the sound of a car driving past or footsteps sounds on a wooden floor. It can make VR experiences feel more real.

Dynamic foveated rendering is an AI-powered technique that can improve the performance of VR experiences by rendering only the parts of the image that the user is looking at in high resolution. It can reduce eye strain and make VR experiences more comfortable to use.

Generative AI can create new content for VR experiences, such as characters, objects, and environments. It can create avatars and digital characters that respond more naturally to users' behavior and emotions. It makes engagement and interactions more engaging and drives the user experience. For example, AI-powered avatars will create realistic interactions between players, making the game more immersive and enjoyable.

Applications Of AI-Powered VR

AI-powered VR has the potential to revolutionize many industries, including healthcare, training, entertainment, and education.It can help to improve safety, reduce costs, and improve outcomes. The use of AI-powered VR in these industries is still in its early stages, but the potential benefits are significant.


VR is being used to create interactive and engaging learning experiences. AI can generate personalized content that adapts to an individual’s learning styles and progress, providing a more effective and personalized educational experience. VR can help increase student attention and engagement, as students are more likely to be interested and engaged with what they are learning when they are immersed in a virtual environment. VR can also transport students to different environments, allowing them to learn and explore various concepts safely and efficiently. 

VR can also provide students with hands-on learning experiences. It can be especially beneficial for STEM subjects, as students can use VR to simulate experiments and procedures that would be difficult or dangerous to perform in the real world. VR can also be used to create virtual field trips, allowing students to visit historical landmarks and other places they would not otherwise be able to see. Students can learn at their own pace and in a way that is most effective for them.

Medical Training

VR and AR are being used in medical training to create realistic simulations for medical students and surgeons to practice on. AI can generate variations in patients' conditions, so students can experience various medical scenarios and learn how to respond appropriately. For example, VR can simulate surgery. Students can practice the steps of a surgery on a virtual patient without the risk of harming a real patient. AI-powered VR can also simulate complex procedures, such as brain surgery.

In addition to surgery, VR can train medical students in other areas, such as diagnostic imaging and patient communication. VR can create realistic simulations of medical imaging machines, so that students can practice interpreting images. AI-powered VR can also create simulations of patient interactions so students can practice their communication skills. VR is a valuable tool for medical training. It allows students to practice procedures and skills in a safe and controlled environment. It can help to improve patient safety and outcomes.


AI-powered VR and AR are being used in marketing to create immersive and personalized experiences for consumers. AI can create virtual product showrooms where consumers interact with products in real time. It helps consumers make informed purchase decisions. For example, Wayfair uses AI to create virtual product showrooms where consumers can see how furniture will look in their homes.

Another application of AI is in lead generation. It can generate leads by creating interactive VR and AR experiences that capture consumers' attention and encourage them to provide their contact information. For example, BMW uses AI to create a VR experience that allows consumers to virtually test drive cars. If consumers are interested in learning more about a particular car, they can provide their contact information to receive more information.


AI-powered VR creates more immersive and engaging entertainment experiences. For example, AI can generate realistic virtual characters, create interactive worlds, personalized experience and game mechanics. AI creates more realistic and challenging VR games. For example, in Beat Saber, AI tracks the player's movements and adjusts the game difficulty accordingly. IT ensures that the game is always challenging, but not impossible to beat.

AI can create virtual tours of real-world locations. For example, the company Google Earth VR uses AI to create photorealistic 360-degree images of cities and landmarks around the world. It can create virtual concerts that allow fans to experience a live show from the comfort of their own homes. For example, the company MelodyVR uses AI to create virtual concerts featuring high-quality sound and visuals.

Use Cases Of AI-Powered VR

Having understood the techniques and methods of integrating AI in VR, let us look at some use cases. From realistic character behaviors driven by AI to real-time object detection for interactive environments, AI-powered VR is changing the world.

Roblox AI for Virtual World Creation

Roblox is harnessing Generative AI to reshape content creation. Its Roblox Studio, a tool for crafting 3D experiences, will receive a boost from Generative AI, redefining how users craft immersive worlds. By mastering patterns and structures, Generative AI accelerates media creation - images, audio, code, text, and 3D models. This integration empowers creators by bridging skill gaps, and fostering groundbreaking innovations. Roblox envisions integrated 3D objects with innate behavior, simplifying interactive content development. Responsible and ethical AI implementation is paramount, ensuring a secure and diverse environment. Roblox's Generative AI sets the stage for a visionary era in content creation.

Generative AI for VR Gaming by Unity

CEO of Unity Software Inc., John Riccitiello, revealed plans for a generative AI marketplace tailored for game developers. This visionary space is set to simplify game creation by offering AI-generated assets - characters, sounds, and more. based on player input. Riccitiello envisions AI-generated game characters complete with motivations, personalities, and objectives - all without human intervention. Unity has already granted developers early access to its forthcoming AI resources, although the marketplace launch timeline remains undisclosed. With tools like DALL-E and Stable Diffusion crafting images, and emerging products concocting videos and game content from text inputs, Unity aims to reshape game development, offering efficiency and accessibility to creators.

NVIDIA for Generative AI

NVIDIA advances in generative AI and graphics. It is integrating OpenAI's ChatGPT to help users generate 3D models and 3D environments. NVIDIA is also using generative AI to make NPCs more intelligent. Their AI marketplace aids game developers with AI-generated assets, streamlining content creation. NVIDIA partnered with Hugging Face for AI training. AI Enterprise 4.0 integrates NVIDIA NeMo for large-scale generative AI models. The NVIDIA AI Workbench offers flexibility across platforms. Omniverse's growth is transforming industries. The GH200 Grace Hopper platform enhances generative AI capabilities. NVIDIA shapes a future where diverse sectors harness AI's potential.

Challenges In Traditional Virtual Reality

Traditional VR technologies rely on headsets to create an immersive experience. It impacts the level of realism. The technology has many challenges, including accessibility, adaptability and user discomfort. Exploring them will help you understand and appreciate the need for AI-powered VR.

Technical Limitations

VR requires high-resolution displays, fast processing, and robust graphics to render realistic images. But, hardware technology may not always be able to meet these requirements. VR headsets face limitations in achieving high resolution and pixel densities. Low processing power reduces frame rates, visual quality, and immersion. High latency in VRs affects user experience by delaying user input and system response. Also, scarce and incompatible software can hinder quality and experience across different platforms and devices.

Customization and Adaptability

Traditional VR systems lack personalization. They might not be comfortable for everyone, as headsets can be heavy or bulky, and lenses might not be the right prescription for some users. Additionally, they mostly allow single-user experiences and offer very limited interactivity. Traditional VR environments are developed in a studio and lack a sense of presence, as they do not provide enough sensory information to the user to make them experience the virtual world to its full potential.

Content and Software Optimization

Traditional VR is limited by the need for content and software optimization. VR requires high-resolution graphics and high frame rates. It can strain the computational resources of VR devices and systems. Hence, VR content and software must be optimized to ensure they can be rendered and displayed in real time without sacrificing quality. It is a challenging and time-consuming process. It can limit the development of VR content and software.


Cybersickness is a type of motion sickness experienced due to immersive exposure to extended reality technologies. It can cause headache, disorientation, nausea, eyestrain and sweating, with symptoms lasting for minutes to hours. The symptoms may vary depending on the type of immersion. For VR exposure, the probability of disorientation is higher than nausea which is higher than oculomotor disturbance symptoms. It is a problem that hinders use of VR technology by a large audience.

Challenges And Limitations Of AI-Powered VR

Challenges and limitations that must be addressed before AI-generated content can be widely adopted for VR applications. The data required to train AI algorithms for VR is often difficult to obtain. For example, generating accurate 3D models of real-world objects and environments requires extensive data collection and processing, which can be time-consuming and costly. Also, creating complex algorithms that can generate realistic and engaging VR content requires significant expertise and computational resources. This can be a barrier to entry for smaller developers or organizations.

Integrating AI-generated content with existing VR systems can also be a challenge. AI-generated content must be compatible with existing hardware and software platforms, which can require significant development and testing. As AI becomes more advanced, there is a risk of creating content that is too realistic or engaging, which could lead to unintended consequences. Developers must consider issues such as user safety and privacy when creating AI-generated content for VR.

The Future Of Generative AI In Extended Reality

AI is rapidly developing and will play a major role in the future of XR. AI is being experimented with to create virtual shopping and traveling experiences. It is playing a role in the development of the metaverse. AI can create digital twins of real-world objects and environments for more realistic metaverse experiences. AI can also create virtual assistants that help users navigate the metaverse and interact with other users.

Some upcoming projects using AI in XR include Apple Vision Pro, a new AI chip that could power more advanced features in its products, such as augmented reality and facial recognition. Recently, Researchers at UT Austin developed a VR headset with EEG sensors to measure brain activity. It allows for unprecedented insights into how humans process stimuli in VR. The technology has potential applications in human-robot interaction and brain-machine interfaces.

AI has the potential to revolutionize the way we shop, travel, and interact with the world around us. As AI continues to develop, we can expect to see even more amazing and innovative applications of AI in XR in the coming years.

Want To Build AI-integrated VR For Your Business?

If you are looking to integrate AI into virtual reality to boost your business or integrate VR into your games, we can help. We are a team of AI engineers with experience in virtual reality and AI. Contact us today and let us create AI-powered VR applications to elevate your business.

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