arXiv Machine Learning
Sep 7

UniMate: One Unified Model to Animate Diverse Skeletons

UniMate is a unified foundation model that generates articulated motion for any skeleton from a rigged 3D asset and a text prompt, eliminating the need for test‑time optimization or per‑skeleton retraining. It uses a topology‑aware diffusion transformer that incorporates skeletal topology through graph‑aware attention bias, spectral rotary position embedding, and a global topological conditioner. Trained on the newly curated UniML3D dataset of 13,006 diverse motion sequences, UniMate outperforms existing baselines in quality, generalization, and efficiency, and supports zero‑shot cross‑topology transfer, in‑betweening, expansion, and text‑guided editing.

By Linzhan Mou, Jiahui Lei, Zhiyang Dou, Chenyue Cai, Chaoyue Song, Adam Finkelstein, Szymon Rusinkiewicz
arXiv Computer Vision
Sep 15

MoCapAnything V2: End-to-End Motion Capture for Arbitrary Skeletons

arXiv:2604.28130v4 Announce Type: replace Abstract: Recent methods for arbitrary-skeleton motion capture from monocular video follow a factorized pipeline, where a Video-to-Pose network predicts join...

By Kehong Gong, Zhengyu Wen, Dao Thien Phong, Mingxi Xu, Weixia He, Qi Wang, Ning Zhang, Zhengyu Li, Guanli Hou, Dongze Lian, Xiaoyu He, Mingyuan Zhang, Hanwang Zhang
arXiv AI
Aug 28

A Unified Conditional Flow for Motion Generation, Editing, and Intra-Structural Retargeting

The paper introduces a unified conditional-flow framework that integrates text-driven motion generation, semantic editing, and intra-structural retargeting into a single rectified-flow model. By treating editing as a change in semantic condition and retargeting as a change in skeletal condition, the approach eliminates fragmented pipelines and allows a single model to perform generation, zero‑shot editing, and zero‑shot retargeting on articulated 3D motion data. Experiments on SnapMoGen and a Mixamo subset demonstrate that the model can handle all three tasks without task‑specific fine‑tuning, preserving both motion semantics and skeletal structure.

By Junlin Li, Xinhao Song, Siqi Wang, Haibin Huang, Yili Zhao
arXiv AI
Jul 1

A Scalable Whole-body Motion Transfer via Implicit Kinodynamic Motion Retargeting

arXiv:2509. 15443v2 Announce Type: replace-cross Abstract: Human-to-humanoid imitation learning presents a promising pathway to address the severe data scarcity bottleneck in robotics by utilizing abundant, large-scale human motion collections.

By Xingyu Chen, Hanyu Wu, Sikai Wu, Mingliang Zhou, Diyun Xiang, Haodong Zhang, Yangchen Zhou, Yukang Gao, Yi Gu, Renjing Xu