Self-Intersection-Aware 3D Human Motion Generation Using an Efficient Human Sphere Proxy


Pascal Herrmann (Bosch Research), Maarten Bieshaar (Bosch Research), Dennis Mack (Bosch Research), Paul Robert Herzog (Bosch Research), Juergen Gall (University of Bonn)
The 35th British Machine Vision Conference
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Abstract

Human motion generation has made tremendous progress in recent years, with state-of-the-art approaches surpassing ground truth data in leading evaluation benchmarks. However, visual inspection of the generated motions paints a different picture. Even state-of-the-art approaches generate motions frequently containing self-intersections, i.e., body parts interpenetrating, which are strong artifacts, severely limiting the perceived motion quality. We introduce a novel loss, which explicitly penalizes self-intersections, to the training of human motion generation methods. We base our loss on a sphere proxy of human geometry, which allows us to calculate a self-intersection loss 98% faster and uses 83% less memory than comparable methods based on triangular meshes. The loss is agnostic to the specific approach, and we add it to the training of the recent human motion generation methods human motion diffusion model (MDM) and MoMask. Our extensive experiments show a reduction of self-intersections in generated motions of up to 49% while improving other evaluation metrics. The code is available at https://github.com/boschresearch/humansphereproxy.

Citation

@inproceedings{Herrmann_2025_BMVC,
author    = {Pascal Herrmann and Maarten Bieshaar and Dennis Mack and Paul Robert Herzog and Juergen Gall},
title     = {Self-Intersection-Aware 3D Human Motion Generation Using an Efficient Human Sphere Proxy},
booktitle = {36th British Machine Vision Conference 2025, {BMVC} 2025, Sheffield, UK, November 24-27, 2025},
publisher = {BMVA},
year      = {2025},
url       = {https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_737/paper.pdf}
}


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