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Advancing Body Tracking in Virtual Reality

A new method enhances full body tracking for immersive virtual experiences.

Denys Rozumnyi, Nadine Bertsch, Othman Sbai, Filippo Arcadu, Yuhua Chen, Artsiom Sanakoyeu, Manoj Kumar, Catherine Herold, Robin Kips

― 8 min read


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Table of Contents

In the world of virtual and augmented reality, making sure that the user's body movements are tracked accurately is essential for a real experience. Imagine wearing a headset and having your movements mirrored in the virtual world just like you're doing them in real life! But here’s the catch: Tracking the whole body, especially the legs, is a real puzzle. Current systems often guess what the lower body is doing because they can't see it well, and that can lead to some funny or awkward situations in the virtual world.

The Problem with Current Systems

Most tracking systems today use just three points on the body: the head and the hands. This means they guess how the rest of the body moves. It's like watching a magician who only shows you part of the trick but expects you to believe the whole thing is real!

To fix this, modern virtual reality (VR) and augmented reality (AR) systems use Depth Cameras to gather information about the space around the user. These cameras give a three-dimensional view of the surroundings. Unfortunately, this technology comes with challenges. For example, if the camera can't see a body part, it doesn't know where to place it. So, while you might be dancing away in your living room, the system may think your legs are still! This can make for some very silly scenes when you move your arms but your legs seem to be on vacation.

A New Approach

This is where our new method, which we call XR-MBT, steps in. XR-MBT combines the information from depth cameras with smart training methods to track full body movements in real time. Think of it like adding more characters to a video game; suddenly the game feels alive with action!

We use depth-sensing technology to get a clearer picture of the user's body movements. Instead of just guessing where the legs are, we teach the system to understand the entire body using the depth data it collects. This helps it paint a more accurate picture of what the user is doing, even if some parts are out of sight.

How Does It Work?

So, how does this magical process work? First, we gather the data from the head position and the hand movements. Then, we also take information from the depth sensor, creating a point cloud-a collection of points in space that represent the user’s body. Think of it as a fuzzy cloud that tries to capture your shape!

This cloud, however, isn't perfect. It might miss certain points of your body or get them a little mixed up. Our system uses smart algorithms to learn from this messy cloud data and figure out the best way to track where each body part should be. It’s like teaching a kid to draw a person using all their favorite crayons, even if some colors are missing.

Training the System

To make our method work, we need to teach it using both real-world data and simulated data. We gather a ton of data from people doing different movements, like jumping, kicking, and dancing. Then, we create a set of rules, or a “how-to” guide, for the system. This helps it get better at guessing where each body part should be, even when it’s not fully visible.

By using this combination of real and fake data, we get something called “Self-Supervised Learning.” This fancy term just means we don’t need to label every single piece of data ourselves. The system learns from the data it sees and gets better at its job over time-like a puppy that learns to fetch by playing!

Tracking Full Body Movements

Once trained, XR-MBT can track the full body in Real-time. This means when you move, it can follow along, even if it can’t see your legs all the time. If your leg is hidden behind a table, the system still knows it’s there and can assume where it should be based on the rest of your movements. So, you can kick a virtual soccer ball without looking ridiculous!

But what if your leg does something unexpected? No problem! XR-MBT has a backup plan. It can switch between different methods of tracking to ensure that what it displays in the virtual world is as close to reality as possible. If it loses sight of a leg, it can fill in the blanks with a smart guess based on where your other body parts are.

Real-World Testing

We put our XR-MBT system through various tests to see how well it performs. We compared it with other existing systems to find out if ours really tracks better. To our delight, we found that XR-MBT does a great job! It can keep track of the legs and lower body much better than those old systems that only guess.

When we tested it with real people in real environments, we noticed that XR-MBT could accurately represent a wide range of leg movements. Kicking, running, and other actions looked much more realistic than ever before, making the virtual experience feel genuine!

The Fun Factor

Now, let’s talk about the fun part! Imagine playing a game where you can run, jump, and kick like a superhero, and the game reflects every move you make. That’s what XR-MBT aims to provide. It opens the doors to a whole new world of entertainment where you are the main star.

Whether you are dancing at a virtual party or participating in a fancy ninja training course, our system helps make those experiences feel just right. Perhaps your virtual buddy won’t be able to keep a straight face when you kick that ball over the fence, and that’s part of the fun!

Conclusion

The world of XR is filled with potential, and accurate body tracking is vital to unlocking it. With XR-MBT, we have taken a step towards creating a system that can faithfully follow your every move-even those sneaky leg movements that were previously left to the imagination. So, whether you're racing through a digital landscape or simply trying to wave hello to your friend, XR-MBT is here to make sure you look good doing it. Now, go ahead and get moving; the virtual world is waiting for you!

Future Developments

As with any technology, there's always room for improvement. While XR-MBT does pretty well, it’s always on the lookout for better ways to track movement. For instance, incorporating more sensors could improve accuracy further. Imagine a future where every twist and turn in your body is captured perfectly, leading to an even more immersive experience.

Also, as XR technology progresses, finding ways to make these systems more user-friendly will be a focus. The goal is to have people step into XR environments without needing a manual; it should just work. That would be like putting on a pair of shoes that magically fit perfectly every time!

Embracing the Unpredictable

One exciting aspect of XR-MBT is its ability to handle the unpredictable nature of human movement. We’re not robots; sometimes we trip over our feet or get tangled in the yoga mat! Our system could be trained to adapt to those little slip-ups, preserving the realism and helping users feel more connected to their virtual surroundings.

A Playground of Possibilities

Imagine various scenarios where XR-MBT could shine. Sports training, dance classes, or even just having fun with friends in a virtual hangout can become far more engaging than ever before. Plus, it can contribute to wellness by letting people explore fitness in a virtual setting, making exercising feel like playtime rather than a chore.

Learning from Mistakes

The learning process doesn't stop once XR-MBT is out there in the world. Every time a user interacts with the system, we gather valuable feedback. We're talking about lessons learned from the virtual playground, whether they're related to movement accuracy or just plain old fun. This will help us continually fine-tune XR-MBT and ensure it remains a top player in the tracking game.

The Bottom Line

At the end of the day, XR-MBT represents a significant leap forward in the way we experience virtual environments. By bridging the gap between the real and the virtual world, we hope to create experiences that are not only engaging and realistic but also fun. So, whether you’re leaping over digital obstacles or just lounging in your virtual living room, rest assured that we’re working hard to make those experiences the best they can be.

So, gear up, put on your headset, and get ready to navigate the world of XR like never before! It’s going to be a ride filled with movement, surprises, and a lot of fun!

Original Source

Title: XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration

Abstract: Tracking the full body motions of users in XR (AR/VR) devices is a fundamental challenge to bring a sense of authentic social presence. Due to the absence of dedicated leg sensors, currently available body tracking methods adopt a synthesis approach to generate plausible motions given a 3-point signal from the head and controller tracking. In order to enable mixed reality features, modern XR devices are capable of estimating depth information of the headset surroundings using available sensors combined with dedicated machine learning models. Such egocentric depth sensing cannot drive the body directly, as it is not registered and is incomplete due to limited field-of-view and body self-occlusions. For the first time, we propose to leverage the available depth sensing signal combined with self-supervision to learn a multi-modal pose estimation model capable of tracking full body motions in real time on XR devices. We demonstrate how current 3-point motion synthesis models can be extended to point cloud modalities using a semantic point cloud encoder network combined with a residual network for multi-modal pose estimation. These modules are trained jointly in a self-supervised way, leveraging a combination of real unregistered point clouds and simulated data obtained from motion capture. We compare our approach against several state-of-the-art systems for XR body tracking and show that our method accurately tracks a diverse range of body motions. XR-MBT tracks legs in XR for the first time, whereas traditional synthesis approaches based on partial body tracking are blind.

Authors: Denys Rozumnyi, Nadine Bertsch, Othman Sbai, Filippo Arcadu, Yuhua Chen, Artsiom Sanakoyeu, Manoj Kumar, Catherine Herold, Robin Kips

Last Update: 2024-11-27 00:00:00

Language: English

Source URL: https://arxiv.org/abs/2411.18377

Source PDF: https://arxiv.org/pdf/2411.18377

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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