通过自适应高斯点云实现更逼真的3D人脸动画与重建。
GeoAvatar: Adaptive Geometrical Gaussian Splatting for 3D Head Avatar
- 按面部区域动态分配刚性/柔性高斯点，提升几何适应性。
- 引入嘴部结构与分段变形策略，显著改善表情动画质量。
- 适合追求高保真人脸建模与动态表达的科研与应用开发者。
尽管3D人脸头像生成取得进展，但如何在保持身份一致（重建）的同时实现新姿态和表情（动画）仍具挑战。现有方法难以适应面部不同区域的几何变化，导致质量不佳。为此，我们提出GeoAvatar，一种自适应几何高斯点云渲染框架。GeoAvatar采用无监督的自适应预分配阶段（APS），将高斯点划分为刚性与柔性集合，以实现自适应偏移正则化；基于嘴部解剖结构与运动特性，提出新型嘴部结构及分段变形策略，增强嘴部动画保真度；此外，设计正则化损失，精确对齐高斯点与3DMM人脸。同时发布包含高度表达性面部动作的DynamicFace视频数据集。大量实验表明，GeoAvatar在重建与新姿态动画场景中优于当前最先进方法。
原文摘要 · Abstract (English)
Despite recent progress in 3D head avatar generation, balancing identity preservation, i.e., reconstruction, with novel poses and expressions, i.e., animation, remains a challenge. Existing methods struggle to adapt Gaussians to varying geometrical deviations across facial regions, resulting in suboptimal quality. To address this, we propose GeoAvatar, a framework for adaptive geometrical Gaussian Splatting. GeoAvatar leverages Adaptive Pre-allocation Stage (APS), an unsupervised method that segments Gaussians into rigid and flexible sets for adaptive offset regularization. Then, based on mouth anatomy and dynamics, we introduce a novel mouth structure and the part-wise deformation strategy to enhance the animation fidelity of the mouth. Finally, we propose a regularization loss for precise rigging between Gaussians and 3DMM faces. Moreover, we release DynamicFace, a video dataset with highly expressive facial motions. Extensive experiments show the superiority of GeoAvatar compared to state-of-the-art methods in reconstruction and novel animation scenarios.
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