arXiv:2604.26917v2 Announce Type: replace
Abstract: Recent advances in 4D content generation have attracted increasing attention, yet creating high-quality animated 3D models remains challenging due...
By Zijie Wu, Chaohui Yu, Fan Wang, Xiang Bai
arXiv:2608.31113v1 Announce Type: new
Abstract: We introduce BLARM, a feed-forward method for video-driven 3D mesh animation. Given a monocular video and a static object mesh, BLARM predicts a tempor...
By Pradyumn Goyal, Yizhak Ben-Shabat, Hsueh-Ti Derek Liu, Haomiao Jiang, Snehasish Mukherjee, Kyle Spence, Mark Stauber, Evangelos Kalogerakis, Yunze Zeng
arXiv:2608.20699v1 Announce Type: new
Abstract: Animating articulated 3D meshes via text requires satisfying strict kinematic constraints, modeling causal interactions between parts, and achieving in...
By Chunyu Zou, Peng Dai, Yi-Hua Huang, Ze Yuan, Jingwei Huang, Yeming Yao, Xiaojuan Qi
Kirin is a new framework that reconstructs 3D animal motion from in‑the‑wild videos, learns motion priors at scale, and generates realistic motion conditioned on text and image. It introduces AiM3D, the first large‑scale dataset of aligned video‑text‑motion tuples for quadruped animals, and uses an off‑the‑shelf image‑to‑3D model to automatically rig and animate 3D meshes with the generated motion. The framework and dataset provide a foundation for large‑scale, text and image‑conditioned animal motion generation and animation.
By Brian Nlong Zhao, Zhuoyang Pan, James M. Rehg, Jiajun Wu, Shangzhe Wu
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