# RigMo: Unifying Rig and Motion Learning for Generative Animation

arXiv: https://arxiv.org/abs/2601.06378 · HTML: https://mlsanity.com/paper/2601.06378/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2026.

## Built on by (most cited first, 5 of 5)

- [ViPS: Video-informed Pose Spaces for Auto-Rigged Meshes](https://mlsanity.com/paper/2604.17623/) (2026)
- [Sketch2Motion: Text-driven 2D Sketch to 3D Animation via Diffusion-guided Skeleton Optimization](https://mlsanity.com/paper/2605.28394/) (2026)
- [GaussiAnimate: Rig Animatable Categories with Level of Dynamics](https://mlsanity.com/paper/2604.08547/) (2026)
- [RegHead: Non-Humanoid Head Blendshapes via Feed-Forward Registration](https://mlsanity.com/paper/2607.12206/) (2026)
- [BLARM: Animating 3D Objects from Video via Blending Latent Rigid Motion Primitives](https://mlsanity.com/paper/2608.31113/) (2026)

## Abstract (arXiv)

Despite significant progress in 4D generation, rig and motion, the core structural and dynamic components of animation are typically modeled as separate problems. Existing pipelines rely on ground-truth skeletons and skinning weights for motion generation and treat auto-rigging as an independent process, undermining scalability and interpretability. We present RigMo, a unified generative framework that jointly learns rig and motion directly from raw mesh sequences, without any human-provided rig annotations. RigMo encodes per-vertex deformations into two compact latent spaces: a rig latent that decodes into explicit Gaussian bones and skinning weights, and a motion latent that produces time-varying SE(3) transformations. Together, these outputs define an animatable mesh with explicit structure and coherent motion, enabling feed-forward rig and motion inference for deformable objects. Beyond unified rig-motion discovery, we introduce a Motion-DiT model operating in RigMo's latent space and demonstrate that these structure-aware latents can naturally support downstream motion generation tasks. Experiments on DeformingThings4D, Objaverse-XL, and TrueBones demonstrate that RigMo learns smooth, interpretable, and physically plausible rigs, while achieving superior reconstruction and category-level generalization compared to existing auto-rigging and deformation baselines. RigMo establishes a new paradigm for unified, structure-aware, and scalable dynamic 3D modeling.