Rigging and skinning are essential steps to create realistic 3D animations, often requiring significant expertise and manual effort. Traditional attempts at automating these processes rely heavily on geometric heuristics and often struggle with objects of complex geometry. Recent data-driven approaches show potential for better generality, but are often constrained by limited training data. We present the Anymate Dataset, a large-scale dataset of 230K 3D assets paired with expert-crafted rigging and skinning information -- 70 times larger than existing datasets. Using this dataset, we propose a learning-based auto-rigging framework with three sequential modules for joint, connectivity, and skinning weight prediction. We systematically design and experiment with various architectures as baselines for each module and conduct comprehensive evaluations on our dataset to compare their performance. Our models significantly outperform existing methods, providing a foundation for comparing future methods in automated rigging and skinning. Code and dataset can be found atthis https URL.
View on arXiv@article{deng2025_2505.06227,
title={ Anymate: A Dataset and Baselines for Learning 3D Object Rigging },
author={ Yufan Deng and Yuhao Zhang and Chen Geng and Shangzhe Wu and Jiajun Wu },
journal={arXiv preprint arXiv:2505.06227},
year={ 2025 }
}