让3D角色绑定在动态序列中保持稳定，不依赖预设姿势。
SPRig: Self-Supervised Pose-Invariant Rigging from Mesh Sequences
- 通过跨帧一致性约束，让绑定模型适应任意姿态
- 在动态序列上实现更连贯的骨骼与权重生成，减少抖动和失真
- 适合做动画、虚拟人等需要时序稳定的场景
当前先进的绑定方法通常假设存在预定义的中性静止姿态（如T-pose），但该假设在动态网格序列（如DyMesh或DT4D）中不成立。若逐帧独立应用现有方法，缺乏姿态不变性，常导致时序不一致的拓扑结构。为此，我们提出SPRig，一种通用的微调框架，通过跨帧一致性约束，在现有模型基础上学习姿态不变的绑定方案，涵盖骨骼与蒙皮生成。针对骨骼生成，我们在令牌空间和几何空间引入新颖的一致性正则化；针对蒙皮，通过不变于动作的一致性损失、一致性蒸馏与结构正则化提升时序稳定性。大量实验表明，SPRig显著提升了时序连贯性，大幅减少前代方法的伪影，且未牺牲甚至提升了单帧静态生成质量。代码见附录，将随论文正式发布。
原文摘要 · Abstract (English)
State-of-the-art rigging methods typically assume a predefined canonical rest pose. However, this assumption does not hold for dynamic mesh sequences such as DyMesh or DT4D, where no canonical T-pose is available. When applied independently frame-by-frame, existing methods lack pose invariance and often yield temporally inconsistent topologies. To address this limitation, we propose SPRig, a general fine-tuning framework that enforces cross-frame consistency across a sequence to learn pose-invariant rigs on top of existing models, covering both skeleton and skinning generation. For skeleton generation, we introduce novel consistency regularization in both token space and geometry space. For skinning, we improve temporal stability through an articulation-invariant consistency loss combined with consistency distillation and structural regularization. Extensive experiments show that SPRig achieves superior temporal coherence and significantly reduces artifacts in prior methods, without sacrificing and often even enhancing per-frame static generation quality. The code is available in the supplemental material and will be made publicly available upon publication.
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