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.

翻译：紧贴表面的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"骨架-蒙皮绑定系统"——Skelebones，包含三个关键步骤：（1）骨骼：将时间一致的可变形高斯体压缩为自由形态骨骼，近似非刚性表面形变；（2）骨架：从规范高斯体中提取平均曲率骨架并进行时间维度优化，确保与类别无关、运动自适应且拓扑正确的运动学结构；（3）绑定：通过非参数化部件级运动匹配（PartMM）实现骨架与骨骼的绑定，通过匹配、检索和混合现有运动合成新颖骨骼运动。这三个步骤共同将4D形状的动力学层次压缩为紧凑且兼具可控性与表现力的Skelebones。我们在合成数据集与真实数据集上验证了该方法，在未见姿态下的重动画性能取得显著提升——相比线性混合蒙皮（LBS）提高17.3% PSNR，相比骨骼袋（BoB）提高21.7%——同时保持优异的重建保真度，尤其适用于呈现复杂非刚性表面动力学的角色。我们的部件级运动匹配算法在高斯体与网格表示上均展现强泛化能力，在低数据场景（约1000帧）中相比鲁棒LBS实现48.4% RMSE降低，并优于基于GRU和MLP的学习方法超过20%。代码将在cookmaker.cn/gaussianimate公开发布用于研究目的。