ARTIST: Adaptive Humanoid Rigging by Transferring Individual Style with Optimal Transport
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Date
2026
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Automatic rigging transforms static meshes into articulated characters by predicting skeletal structure. However, rigging is inherently subjective: artists develop personal preferences for joint placement. Current approaches omit this aspect, learning only the average “style” of their training data. We quantify inter-artist variance through a user study and dataset analysis, demonstrating this notion of “rigging style”. We propose a voxel-based model leveraging pretrained 3D backbones that outperforms state-of-the-art methods. We also introduce a one-shot style adaptation method based on volumetric optimal transport: given a single artist-rigged example, we transfer its stylistic joint placements to any new character. This improves any rigging model and supports different bone counts or hierarchies, reconciling automatic rigging with artistic variability.
Description
@inproceedings{10.2312:egs.20261001,
booktitle = {Eurographics 2026 - Short Papers},
editor = {Musialski, Przemyslaw and Lim, Isaak},
title = {{ARTIST: Adaptive Humanoid Rigging by Transferring Individual Style with Optimal Transport}},
author = {Lefèvre, Jeanne-Emma and Cheynel, Théo and El Khalifi, Omar and Daniel, Thomas and Bellot-Gurlet, Baptiste},
year = {2026},
publisher = {The Eurographics Association},
ISSN = {2309-5059},
ISBN = {978-3-03868-299-8},
DOI = {10.2312/egs.20261001}
}