Gaussian Pixel Codec Avatars: A Hybrid Representation for Efficient Rendering

• URL: http://arxiv.org/abs/2512.15711v1
• Date: Wed, 17 Dec 2025 18:58:50 GMT
• Title: Gaussian Pixel Codec Avatars: A Hybrid Representation for Efficient Rendering
• Authors: Divam Gupta, Anuj Pahuja, Nemanja Bartolovic, Tomas Simon, Forrest Iandola, Giljoo Nam,
• Abstract summary: GPiCA utilizes a unique hybrid representation that combines a triangle mesh and anisotropic 3D Gaussians.<n>We train neural networks to decode a facial expression code into three components: a 3D face mesh, an RGBA texture, and a set of 3D Gaussians.<n>Our results demonstrate that GPiCA achieves the realism of purely Gaussian-based avatars while matching the rendering performance of mesh-based avatars.
• Score: 11.508015004156391
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present Gaussian Pixel Codec Avatars (GPiCA), photorealistic head avatars that can be generated from multi-view images and efficiently rendered on mobile devices. GPiCA utilizes a unique hybrid representation that combines a triangle mesh and anisotropic 3D Gaussians. This combination maximizes memory and rendering efficiency while maintaining a photorealistic appearance. The triangle mesh is highly efficient in representing surface areas like facial skin, while the 3D Gaussians effectively handle non-surface areas such as hair and beard. To this end, we develop a unified differentiable rendering pipeline that treats the mesh as a semi-transparent layer within the volumetric rendering paradigm of 3D Gaussian Splatting. We train neural networks to decode a facial expression code into three components: a 3D face mesh, an RGBA texture, and a set of 3D Gaussians. These components are rendered simultaneously in a unified rendering engine. The networks are trained using multi-view image supervision. Our results demonstrate that GPiCA achieves the realism of purely Gaussian-based avatars while matching the rendering performance of mesh-based avatars.

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