We study 4D generation to synthesize temporally coherent sequences of 3D geometry for animation and content creation. In contrast to existing SDS-based optimization methods and video-driven animation approaches, we adopt a skeleton-driven animation framework aligned with standard industrial pipelines, which enables explicit control and editing.
The paper introduces the Elastic Shape Variational Autoencoder (ES‑VAE), a geometry‑aware generative model for skeletal pose trajectories that uses the transported square‑root velocity field representation on Kendall's shape manifold to remove rigid transformations and temporal rate variability. ES‑VAE maps sequences to a low‑dimensional latent space via the Riemannian logarithm map and reconstructs them using the exponential map. Experiments on gait analysis for clinical mobility scoring and action recognition on the NTU RGB+D dataset show that ES‑VAE outperforms standard VAEs and several sequence‑modeling baselines.
By Arafat Rahman, Shashwat Kumar, Laura E. Barnes, Anuj Srivastava
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
UniMo introduces a unified point‑cloud based framework for generating 3D motion that works for both humans and animals, overcoming challenges posed by diverse skeletal topologies and limited animal datasets. It converts parametric skeletons into unparametric representations and uses dynamic sampling to focus on active joints. The authors also release UniML3D, a large motion‑language dataset with 145,907 sequences and 433,388 captions, and demonstrate state‑of‑the‑art performance on multiple benchmarks.
By Zeyu Zhang, Zhiyuan Zhang, Siheng Wang, Yiran Wang, Danning Li, Ian Reid, Richard Hartley
arXiv:2609.38578v1 Announce Type: cross
Abstract: Cross-structural motion retargeting aims to transfer motion between different skeletal topologies. Despite recent progress, existing state-of-the-art...
By Kia-J\"ung Yang, Fabian H. Sinz, Pawe{\l} A. Pierzchlewicz
arXiv:2606.10142v2 Announce Type: replace
Abstract: Recent advances in 3D generation have led to substantial improvements in realism, controllability, and efficiency, yet the evaluation of 3D assets...
By Nanshan Jia, Zhenyu Zhao, Junbo Peng, Sui Huang, Jingshen Wang, Zeyu Zheng