VolumetricSMPL:
A Neural Volumetric Body Model for
Efficient Interactions, Contacts, and Collisions



VolumetricSMPL extends SMPL(-X) models with volumetric capabilities for efficient interaction, contact, and collision modeling across diverse 3D tasks.


Overview

VolumetricSMPL is a lightweight, plug-and-play extension for SMPL(-X) models that adds volumetric functionality via Signed Distance Fields (SDFs). With minimal integration—just a single line of code—users gain access to fast and differentiable SDF queries, collision detection, and self-intersection resolution. The model is fully compatible with existing SMPL-based pipelines and enables efficient interaction modeling in both perception and reconstruction tasks.




Check out our paper for more results and comparisons.

BibTex

@inproceedings{ICCV25:VolumetricSMPL,
   title={{VolumetricSMPL}: A Neural Volumetric Body Model for Efficient Interactions, Contacts, and Collisions},
   author={Mihajlovic, Marko and Zhang, Siwei and Li, Gen and Zhao, Kaifeng and M{\"u}ller, Lea and Tang, Siyu},
   booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
   year={2025}
}


Contact

For questions, please contact Marko Mihajlovic or raise an issue on GitHub.