用可动骨架压缩动态形状,实现高保真可控动画。
GaussiAnimate: Reconstruct and Rig Animatable Categories with Level of Dynamics

- 将动态高斯点云压缩为贴合表面的自由骨骼,捕捉非刚性形变。
- 通过均曲率骨架提取与时间优化,构建自适应运动结构,提升控制性。
- 基于局部运动匹配合成新动作,低数据下表现优于主流方法20%以上。
自由形态骨骼能有效捕捉非刚性形变,但缺乏直观控制所需的运动学结构。为此,我们提出名为「Skelebones」的骨架-皮肤绑定系统,包含三步:(1) 骨骼:将时间一致的可变形高斯压缩为自由形态骨骼,近似表面非刚性变形;(2) 骨架:从标准高斯中提取均曲率骨架并进行时间优化,确保类别无关、运动自适应且拓扑正确的运动结构;(3) 绑定:通过非参数化局部运动匹配(PartMM)将骨架与骨骼绑定,通过匹配、检索和融合现有动作来合成新骨骼运动。三者协同将4D形状的动态层级压缩为紧凑可控的skelebones。我们在合成与真实数据集上验证该方法,在未见姿态下的重动画性能显著提升:相比线性混合皮肤(LBS)提升17.3% PSNR,相比袋骨模型(BoB)提升21.7%;重建保真度优异,尤其在复杂非刚性动态角色上表现突出。局部运动匹配算法对高斯与网格表示均有强泛化能力,尤其在低数据场景(约1000帧)下,相比鲁棒LBS降低48.4% RMSE,优于基于GRU与MLP的学习方法超过20%。代码将公开于 cookmaker.cn/gaussianimate。
原文摘要 · Abstract (English)
Free-form bones, that conform closely to the surface, can effectively capture non-rigid deformations, but lack a kinematic structure necessary for intuitive control. Thus, we propose a Scaffold-Skin Rigging System, termed "Skelebones", with three key steps: (1) Bones: compress temporally-consistent deformable Gaussians into free-form bones, approximating non-rigid surface deformations; (2) Skeleton: extract a Mean Curvature Skeleton from canonical Gaussians and refine it temporally, ensuring a category-agnostic, motion-adaptive, and topology-correct kinematic structure; (3) Binding: bind the skeleton and bones via non-parametric partwise motion matching (PartMM), synthesizing novel bone motions by matching, retrieving, and blending existing ones. Collectively, these three steps enable us to compress the Level of Dynamics of 4D shapes into compact skelebones that are both controllable and expressive. We validate our approach on both synthetic and real-world datasets, achieving significant improvements in reanimation performance across unseen poses-with 17.3% PSNR gains over Linear Blend Skinning (LBS) and 21.7% over Bag-of-Bones (BoB)-while maintaining excellent reconstruction fidelity, particularly for characters exhibiting complex non-rigid surface dynamics. Our Partwise Motion Matching algorithm demonstrates strong generalization to both Gaussian and mesh representations, especially under low-data regime (~1000 frames), achieving 48.4% RMSE improvement over robust LBS and outperforming GRU- and MLP-based learning methods by >20%. Code will be made publicly available for research purposes at cookmaker.cn/gaussianimate.
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