统一UV空间实现头像网格与高斯点云协同优化，兼顾细节与动画一致性。
URHead: A Unified UV-Space Representation for Joint Mesh-3DGS Optimization in Head Avatars
- 在统一的UV空间中融合网格与高斯表示，共享参数化
- 联合优化下自适应采样，提升重建质量与动画稳定性
- 保留参数化控制能力，适合需要精细动画的虚拟人场景
我们提出URHead，一种用于高保真可驱动头像的统一表示方法，从根本上重构了网格与高斯点云的融合方式。网格方法虽能精确控制几何结构但缺乏逼真细节，而高斯方法虽能实现逼真视觉效果却存在结构不一致问题。现有混合方案未能充分发挥两者互补优势。我们的核心贡献在于引入统一的UV空间，使两种表示共享相同的参数化。通过联合优化与自适应高斯采样，方法可自动解耦并合理分配各组件角色。URHead在保持全参数化可控性的同时，保留个体特异性细节，在重建质量与动画一致性上优于现有最先进方法。
原文摘要 · Abstract (English)
We present URHead, a unified representation for high-fidelity and animatable head avatars that fundamentally redefines mesh-Gaussian integration. While mesh-based methods offer precise geometric control but lack photorealistic detail, and Gaussian-based approaches achieve photorealism but suffer from poor structural consistency, existing hybrid solutions fail to fully leverage their complementary strengths. Our key contribution is a UV-space unification where both representations share a common UV parameterization. Through joint optimization with adaptive gaussian sampling, our method automatically learns to disentangle and allocate appropriate roles to each component. URHead maintains full parametric controllability while preserving subject-specific details, and outperforms existing state-of-the-art methods in reconstruction quality and animation consistency.
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