发表机构
Princeton University; University of California, Berkeley; Massachusetts Institute of Technology; Nanyang Technological University（普林斯顿大学; 加利福尼亚大学伯克利分校; 麻省理工学院; 南洋理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
UniMate是统一基础模型，通过拓扑感知扩散Transformer，在UniML3D数据集训练后，实现从3D资产和文本提示生成任意骨架动画，性能优于基线，支持多种动画编辑任务。
AI 中文摘要
近期自动绑定技术的进展已能大规模生成可用于动画的3D资产，但驱动这些资产的动作生成仍是瓶颈。现有学习型动画生成器受拓扑结构限制：它们依赖特定类别的模板，或在推理时需要针对每个骨架进行微调及参考动作。我们提出UniMate，一个统一的基础模型，可从已绑定的3D资产和文本提示中为任意骨架生成关节动作，无需测试时优化或针对每个骨架的重新训练。UniMate引入了拓扑感知扩散Transformer，通过三种机制将骨架拓扑结构融入注意力机制：（1）基于关节对关系和测地距离的图感知注意力偏置；（2）通过图拉普拉斯算子将RoPE泛化到任意运动学树的频谱旋转位置嵌入；（3）从静止姿态骨架注意力池化得到的全局拓扑条件器。我们还整理了UniML3D数据集，包含13006个动作序列，涵盖双足、四足、鸟类、海洋生物、昆虫、蛇形及关节刚性物体，采用统一规范化处理并配有文本配对。在该数据集上训练后，UniMate在质量、泛化性和效率上均优于现有最优基线，支持零样本跨拓扑迁移、动作补全、动作扩展及文本引导编辑。我们的项目页面可在该网址获取。
英文摘要
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/.
CommentsSIGGRAPH Asia 2026. Project page: https://linzhanmou.com/unimate/
