UniMate: One Unified Model to Animate Diverse Skeletons

Hugging Face Daily Papers Papers
animation diffusion-transformer skeleton-animation topology-aware zero-shot-learning 3d-models

Summary

UniMate is a unified diffusion transformer model that generates articulated motion for diverse skeletons from text and rigged 3D assets without per-skeleton retraining, using topology-aware attention and a large curated dataset.

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. We present UniMate, a unified foundation model that synthesizes articulated motion for arbitrary skeletons from a rigged 3D asset and a text prompt, with no test-time optimization or per-skeleton retraining. UniMate introduces a topology-aware diffusion transformer, which integrates skeletal topology into attention via three mechanisms: (1) a graph-aware attention bias from pairwise joint relations and geodesic distances; (2) a spectral rotary position embedding generalizing RoPE to arbitrary kinematic trees via the graph Laplacian; and (3) a global topological conditioner attention-pooled from the rest-pose skeleton. We also curate UniML3D, 13,006 motion sequences spanning bipedal, quadrupedal, avian, marine, insectoid, serpentine, and articulated rigid objects with unified canonicalization and text pairing. Trained on this dataset, UniMate outperforms state-of-the-art baselines in quality, generalization, and efficiency, and supports zero-shot cross-topology transfer, in-betweening, expansion, and text-guided editing. Our project page is available at https://linzhanmou.com/unimate/.
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Source: https://huggingface.co/papers/2609.05415

Abstract

UniMate is a unified diffusion transformer that generates articulated motion for arbitrary skeletons from text and rigged 3D assets without per-skeleton retraining, using topology-aware attention and a large curated motion dataset.

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. We present UniMate, a unified foundation model that synthesizes articulated motion for arbitrary skeletons from a rigged 3D asset and a text prompt, with no test-time optimization or per-skeleton retraining. UniMate introduces atopology-aware diffusion transformer, which integrates skeletal topology into attention via three mechanisms: (1) agraph-aware attention biasfrom pairwise joint relations and geodesic distances; (2) aspectral rotary position embeddinggeneralizing RoPE to arbitrary kinematic trees via thegraph Laplacian; and (3) aglobal topological conditionerattention-pooled from the rest-pose skeleton. We also curateUniML3D, 13,006 motion sequences spanning bipedal, quadrupedal, avian, marine, insectoid, serpentine, and articulated rigid objects with unified canonicalization and text pairing. Trained on this dataset, UniMate outperforms state-of-the-art baselines in quality, generalization, and efficiency, and supportszero-shot cross-topology transfer,in-betweening, expansion, andtext-guided editing. Our project page is available at https://linzhanmou.com/unimate/.

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