VRGaussianAvatar: Integrating 3D Gaussian Avatars into VR
This work addresses the challenge of creating realistic and efficient avatars for VR users, though it appears incremental as it builds on existing 3D Gaussian Splatting methods.
The authors tackled the problem of enabling real-time full-body 3D Gaussian avatars in virtual reality using only head-mounted display tracking, and the result was a system that sustains interactive VR performance with higher perceived appearance similarity, embodiment, and plausibility compared to baselines.
We present VRGaussianAvatar, an integrated system that enables real-time full-body 3D Gaussian Splatting (3DGS) avatars in virtual reality using only head-mounted display (HMD) tracking signals. The system adopts a parallel pipeline with a VR Frontend and a GA Backend. The VR Frontend uses inverse kinematics to estimate full-body pose and streams the resulting pose along with stereo camera parameters to the backend. The GA Backend stereoscopically renders a 3DGS avatar reconstructed from a single image. To improve stereo rendering efficiency, we introduce Binocular Batching, which jointly processes left and right eye views in a single batched pass to reduce redundant computation and support high-resolution VR displays. We evaluate VRGaussianAvatar with quantitative performance tests and a within-subject user study against image- and video-based mesh avatar baselines. Results show that VRGaussianAvatar sustains interactive VR performance and yields higher perceived appearance similarity, embodiment, and plausibility. Project page and source code are available at https://vrgaussianavatar.github.io.