UniMate：统一模型实现多样化骨骼动画
UniMate: One Unified Model to Animate Diverse Skeletons
UniMate无需测试时优化或每骨架重新训练，就能为任意骨骼生成动画，解决了现有动画师受拓扑限制的问题。
UniMate是一个统一的基础模型，可根据3D资产和文本提示为任意骨骼生成关节运动。该模型引入了拓扑感知扩散变换器，通过三种机制将骨骼拓扑整合到注意力中。研究团队构建了UniML3D数据集，包含13,006个运动序列，涵盖双足、四足、鸟类、海洋生物、昆虫、蛇形和关节刚体。UniMate在质量、泛化性和效率上优于最先进基线，支持零样本跨拓扑转移、中间帧生成、扩展和文本引导编辑。
UniMate: One Unified Model to Animate Diverse Skeletons
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/.
