SPRig: Self-Supervised Pose-Invariant Rigging from Mesh Sequences

• URL: http://arxiv.org/abs/2602.12740v1
• Date: Fri, 13 Feb 2026 09:08:50 GMT
• Title: SPRig: Self-Supervised Pose-Invariant Rigging from Mesh Sequences
• Authors: Ruipeng Wang, Langkun Zhong, Miaowei Wang,
• Abstract summary: State-of-the-art rigging methods assume a canonical rest pose--an assumption that fails for sequential data that lack the T-pose.<n>We propose SPRig, a general fine-tuning framework that enforces cross-frame consistency losses to learn pose-invariant on top of existing models.
• Score: 3.276906364372961
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: State-of-the-art rigging methods assume a canonical rest pose--an assumption that fails for sequential data (e.g., animal motion capture or AIGC/video-derived mesh sequences) that lack the T-pose. Applied frame-by-frame, these methods are not pose-invariant and produce topological inconsistencies across frames. Thus We propose SPRig, a general fine-tuning framework that enforces cross-frame consistency losses to learn pose-invariant rigs on top of existing models. We validate our approach on rigging using a new permutation-invariant stability protocol. Experiments demonstrate SOTA temporal stability: our method produces coherent rigs from challenging sequences and dramatically reduces the artifacts that plague baseline methods. The code will be released publicly upon acceptance.

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