HRAvatar: High-Quality and Relightable Gaussian Head Avatar
This work addresses the challenge of creating realistic 3D head avatars for applications like virtual reality and gaming, though it is incremental as it builds on existing 3D Gaussian Splatting methods.
The paper tackles the problem of reconstructing high-quality, animatable, and relightable 3D head avatars from monocular videos by proposing HRAvatar, which reduces tracking errors and captures facial deformations better, resulting in superior-quality heads and realistic visual effects under varying lighting conditions.
Reconstructing animatable and high-quality 3D head avatars from monocular videos, especially with realistic relighting, is a valuable task. However, the limited information from single-view input, combined with the complex head poses and facial movements, makes this challenging. Previous methods achieve real-time performance by combining 3D Gaussian Splatting with a parametric head model, but the resulting head quality suffers from inaccurate face tracking and limited expressiveness of the deformation model. These methods also fail to produce realistic effects under novel lighting conditions. To address these issues, we propose HRAvatar, a 3DGS-based method that reconstructs high-fidelity, relightable 3D head avatars. HRAvatar reduces tracking errors through end-to-end optimization and better captures individual facial deformations using learnable blendshapes and learnable linear blend skinning. Additionally, it decomposes head appearance into several physical properties and incorporates physically-based shading to account for environmental lighting. Extensive experiments demonstrate that HRAvatar not only reconstructs superior-quality heads but also achieves realistic visual effects under varying lighting conditions.