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SqueezeMe: The Future of VR Avatars

Realistic avatars enhance virtual reality experiences for meetings and gaming.

Shunsuke Saito, Stanislav Pidhorskyi, Igor Santesteban, Forrest Iandola, Divam Gupta, Anuj Pahuja, Nemanja Bartolovic, Frank Yu, Emanuel Garbin, Tomas Simon

― 6 min read


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Table of Contents

In the world of virtual reality (VR), one of the main aims has always been to create realistic human Avatars. These avatars are digital representations of people that can move and interact in a virtual space, just like in real life. The challenge? Making them look good while ensuring they can be driven in Real-time – think of them as your virtual alter egos, ready for action at the drop of a hat.

The Quest for Realism

Imagine entering a virtual meeting or game and seeing life-like avatars interacting with each other. Wouldn't that be thrilling? The goal has been to simulate real human interactions effectively. To do this, avatars need to mirror the looks and movements of real people convincingly. This means they have to behave just like you when you wave your hands, nod your head, or even grimace (we all have those moments, don’t we?).

Traditionally, Rendering such avatars required powerful desktop computers, which made it tough to use them on portable devices like VR headsets. But what if we could create a system that allowed multiple avatars to appear on a headset without compromising on quality? This is where the fun begins!

Enter SqueezeMe

Meet SqueezeMe, a clever approach to creating avatars that look amazing and can be rendered on the go! This technique uses something fancy called Gaussian Splatting, which allows avatars to be both high-quality and efficient. Think of Gaussian splatting as a new way to paint your avatar – it’s like using a digital spray can that can create intricate details like hair and clothing in a way that meshes and older methods can’t quite keep up with.

The Challenge

Previous methods to create these avatars often struggled with real-time performance, requiring heavy computational power. The result was that only one avatar could run at a time on a headset, which is less fun when you want to interact with friends or colleagues in a virtual space. Therefore, the main goal was to develop a system that could operate smoothly on VR headsets, making it possible for multiple avatars to be displayed and manipulated simultaneously.

The Magic of Gaussian Splatting

Gaussian Splatting works by using dots (or "splats") to represent parts of an avatar. Each of these splats carries information such as color and opacity, allowing for smooth animations and transitions. This method is particularly good at showing complex details like the flow of hair or the wrinkles in clothing.

However, the heavy lifting involved in turning this splatting into something that can work on a portable device like a VR headset faced some hurdles. The Decoder, which helps animate the avatars, and the rendering process were identified as the main bottlenecks. Think of these like the traffic jams of the virtual world – we needed to find ways to keep the vehicles (or data) moving smoothly!

Streamlining the Process

To fix these traffic jams, clever techniques were introduced:

  1. Training in UV-space: Instead of using traditional pixel-based training, the avatars were trained in a UV-space, which allows for faster and more efficient processing.

  2. Single Layer Distillation: By simplifying the decoder into a single layer, the process becomes quicker. It's like going from a multi-course meal to a quick snack – still delicious, but much faster!

  3. Neighborly Sharing: Nearby splats can now share a single corrective input from the decoder. Imagine a group of friends. If they all share their pizza rather than each ordering their own, they can save time and resources!

The Result?

When these changes came together, SqueezeMe achieved something incredible: it managed to run three avatars at 72 frames per second (FPS) on the Meta Quest 3 headset. To put that into perspective, that's like juggling three bowling pins while riding a unicycle – impressive coordination and skill!

Realistic Avatars in Action

The avatars created with this system are not just for show; they are driven by real-time video input. So, if you wave your hand in front of a camera, your avatar does the same in the virtual world. This means you can truly express yourself in VR, making meetings, games, and other experiences more engaging.

The Learning Curve

While the journey to creating SqueezeMe has been promising, it isn't without its bumps. For example, some issues still arise in specific areas, particularly with fine details like hands or clothing edges. Sometimes, the avatars can look a little blurry or lose their sharpness. But hey, just like how any superhero has to learn to use their powers, these avatars are still in training!

The Tools Behind the Magic

The technology powering this avatar wizardry isn’t just simple software; it’s heavily intertwined with complex hardware. The Meta Quest 3 headset, for instance, incorporates multiple processing elements including ARM CPUs and a mobile GPU. Together, they allow the avatars to be visualized in real-time in a way that feels almost lifelike.

Real-World Applications

So why does all of this matter? Well, the implications are vast:

  • Meetings and Collaboration: Imagine being able to attend a meeting where your avatar interacts with others, showcasing real emotions and movements. Instead of seeing just a grid of faces on a screen, you'd see a lively representation of everyone involved.

  • Gaming: In multi-player games, having realistic avatars can enhance the experience, making it feel like you are actually battling alongside your friends, instead of just controlling a character on the screen.

  • Social Interaction: Friends could hang out in virtual spaces, with avatars that reflect their moods and personalities almost perfectly.

Conclusion: The Future of Virtual Interaction

As this technology progresses, we can expect to see more lifelike interactions in VR. The ability to create and control realistic avatars locally on devices opens up endless possibilities for gaming, meetings, and more. It’s like stepping into a new dimension where you can truly be yourself, minus the traffic jams of earlier methods.

In the end, SqueezeMe isn't just about making avatars look good; it’s about pushing the boundaries of what’s possible in virtual reality. So, keep an eye out – the world of VR avatars is just getting started, and who knows what kind of fun experiences await just around the corner!

Original Source

Title: SqueezeMe: Efficient Gaussian Avatars for VR

Abstract: Gaussian Splatting has enabled real-time 3D human avatars with unprecedented levels of visual quality. While previous methods require a desktop GPU for real-time inference of a single avatar, we aim to squeeze multiple Gaussian avatars onto a portable virtual reality headset with real-time drivable inference. We begin by training a previous work, Animatable Gaussians, on a high quality dataset captured with 512 cameras. The Gaussians are animated by controlling base set of Gaussians with linear blend skinning (LBS) motion and then further adjusting the Gaussians with a neural network decoder to correct their appearance. When deploying the model on a Meta Quest 3 VR headset, we find two major computational bottlenecks: the decoder and the rendering. To accelerate the decoder, we train the Gaussians in UV-space instead of pixel-space, and we distill the decoder to a single neural network layer. Further, we discover that neighborhoods of Gaussians can share a single corrective from the decoder, which provides an additional speedup. To accelerate the rendering, we develop a custom pipeline in Vulkan that runs on the mobile GPU. Putting it all together, we run 3 Gaussian avatars concurrently at 72 FPS on a VR headset. Demo videos are at https://forresti.github.io/squeezeme.

Authors: Shunsuke Saito, Stanislav Pidhorskyi, Igor Santesteban, Forrest Iandola, Divam Gupta, Anuj Pahuja, Nemanja Bartolovic, Frank Yu, Emanuel Garbin, Tomas Simon

Last Update: 2024-12-20 00:00:00

Language: English

Source URL: https://arxiv.org/abs/2412.15171

Source PDF: https://arxiv.org/pdf/2412.15171

Licence: https://creativecommons.org/licenses/by/4.0/

Changes: This summary was created with assistance from AI and may have inaccuracies. For accurate information, please refer to the original source documents linked here.

Thank you to arxiv for use of its open access interoperability.

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