GeezerVison is showing anymate3d.github.io — Anymate: A Dataset and Baselines for Learning 3D Object Rigging
🪄Anymate: A Dataset and Baselines for Learning 3D Object Rigging
1Stanford University,
2University of Cambridge
( *Equal contribution. †Equal advising. )
Anymate turns static 3D meshes into animatable assets
with automatically predicted rigging and skinning.
Animation Result of Rigging Predicted by Anymate
Abstract
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 de
sign 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.
Animation Result of Rigging Predicted by Anymate
(Sample 1)
(Sample 5)
(Sample 9)
(Sample 2)
(Sample 6)
(Sample 10)
(Sample 3)
(Sample 7)
(Sample 11)
(Sample 4)
(Sample 8)
(Sample 12)
Related Links
There's a lot of excellent work that was introduced around the same time as ours.
RigAnything, MagicArticulate and One Model to Rig Them All
employ autoregressive model for skeleton prediction.
MagicArticulate introduces the Articulation-XL Dataset
derived from Objaverse-XL and comprising over 33,000 models annotated with rigging details.
One Model to Rig Them All contributes two datasets: Rig-XL Dataset a curated subset of about 14,000 rigged models from Objaverse-XL and the
VRoid Dataset, a collection of 2,000 stylized characters from VRoidHub
Together, these works highlight the growing emphasis on scalable, data-driven rigging solutions.
BibTeX
@inproceedings{deng2025anymate,
author = {Yufan Deng and Yuhao Zhang and Chen Geng and Shangzhe Wu and Jiajun Wu},
title = {Anymate: A Dataset and Baselines for Learning 3D Object Rigging},
booktitle = {Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers (SIGGRAPH Conference Papers '25)},
year = {2025},
month = aug,
address = {Vancouver, BC, Canada},
publisher = {Association for Computing Machinery},
location = {Vancouver, BC, Canada},
note = {August 10–14, 2025}
}
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