用姿态引导变形提升真人3DAvatar的动画精度与细节质量
AniGaussian: Animatable Gaussian Avatar with Pose-guided Deformation
- 引入姿态引导的高斯变形策略，结合SMPL先验约束动态形态
- 通过分块缩放策略显著改善几何质量，实现更精细的表面重建
- 适合需要高保真动画人像的虚拟演出、游戏开发场景
基于高斯的人体重建近期在可动画化身生成上取得显著进展，但仍面临未能充分挖掘SMPL模型先验知识及视觉保真度不足的问题。本文提出AniGaussian，通过两个核心思路解决上述挑战：首先，设计了一种新颖的姿态引导变形策略，利用SMPL姿态先验有效约束动态高斯化身，确保重建模型不仅捕捉到表面细节，且在多种动作下保持解剖学合理性；其次，针对高斯模型在动态人体表达上的局限性，引入前期工作的刚性先验以增强动态变换能力，并提出一种分块缩放策略，显著提升几何质量。消融实验证明了模型设计的有效性。与现有方法的广泛对比表明，AniGaussian在定性和定量指标上均表现更优。
原文摘要 · Abstract (English)
Recent advancements in Gaussian-based human body reconstruction have achieved notable success in creating animatable avatars. However, there are ongoing challenges to fully exploit the SMPL model's prior knowledge and enhance the visual fidelity of these models to achieve more refined avatar reconstructions. In this paper, we introduce AniGaussian which addresses the above issues with two insights. First, we propose an innovative pose guided deformation strategy that effectively constrains the dynamic Gaussian avatar with SMPL pose guidance, ensuring that the reconstructed model not only captures the detailed surface nuances but also maintains anatomical correctness across a wide range of motions. Second, we tackle the expressiveness limitations of Gaussian models in representing dynamic human bodies. We incorporate rigid-based priors from previous works to enhance the dynamic transform capabilities of the Gaussian model. Furthermore, we introduce a split-with-scale strategy that significantly improves geometry quality. The ablative study experiment demonstrates the effectiveness of our innovative model design. Through extensive comparisons with existing methods, AniGaussian demonstrates superior performance in both qualitative result and quantitative metrics.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。