UniMate: One Unified Model to Animate Diverse Skeletons
UniMate is a topology-aware diffusion transformer generating articulated motion for arbitrary rigged skeletons from text, trained on 13,006 motion sequences.
UniMate is a unified foundation model that animates arbitrary rigged 3D skeletons from an asset and text prompt with no test-time optimization or per-skeleton retraining. It uses a topology-aware diffusion transformer combining graph-aware attention bias from joint relations and geodesic distances, a spectral rotary position embedding generalizing RoPE to kinematic trees via the graph Laplacian, and a global topological conditioner. The accompanying UniML3D dataset spans 13,006 motion sequences across bipedal, quadrupedal, avian, marine, insectoid, serpentine, and articulated rigid-object skeletons; the model outperforms baselines and supports zero-shot cross-topology transfer, in-betweening, expansion, and text-guided editing.
- Works on arbitrary skeletons without per-skeleton fine-tuning
- Generalizes RoPE to arbitrary kinematic trees via graph Laplacian
- UniML3D dataset: 13,006 sequences across many topologies
- Supports zero-shot cross-topology transfer and text-guided editing
- AI models
- UniMate
From the original article
1 min read at huggingface.co
Recent advances in automatic rigging now deliver animation-ready 3D assets at scale, yet generating the motion to drive them remains a bottleneck. Existing learned animators are topology-constrained: they rely on category-specific templates or require per-skeleton fine-tuning and reference motions at inference.
By Linzhan Mou, Jiahui Lei, Zhiyang Dou · huggingface.co
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