ETCH
Generalizing Body Fitting to Clothed Humans via
Equivariant Tightness

Boqian Li¹, Haiwen Feng^2,3*, Zeyu Cai¹, Michael J. Black², Yuliang Xiu^1,2†

¹Westlake University ²Max Planck Institute for Intelligent Systems ³Berkeley AI Research (BAIR)

^*Project Lead ^†Corresponding Author

Code arXiv Twitter Video

Body Fitting on Clothed Humans. Given 3D clothed humans in any pose and clothing, ETCH accurately fits the body underneath. Our key novelty is modeling cloth-to-body SE(3)-equivariant tightness vectors for clothed humans, abbreviated as ETCH, which resembles "etching" from the outer clothing down to the inner body. The ground-truth body is shown in blue, our fitted body in green, and ground-truth markers as ●.

Video Demonstration of How ETCH Works

Clothed Human Fitting Results

(Scan, Fitted SMPL, 3D Tightness Hotmap)

Method

1. ETCH Pipeline: 1) Equivariant Tightness Vector Prediction, which takes the sampled points X as input, and estimates the tightness directions D via equivariant features f^equiv , along with the tightness magnitudes B, labels L, and confidences C via invariant features f^inv . With these ingredients, in 2) Marker Aggregation and SMPL Optimization, the points move inward along the tightness vectors, forming body-shaped point clouds. These points are weighted and aggregated, based on their labels and confidences, to produce final markers for SMPL fitting.

2. Illustration of our Tightness-Vector and Marker-Confidence system.

BibTeX

@article{li2025etch,
  title={ETCH: Generalizing Body Fitting to Clothed Humans via Equivariant Tightness},
  author={Li, Boqian and Feng, Haiwen and Cai, Zeyu and Black, Michael J and Xiu, Yuliang},
  journal={arXiv preprint arXiv:2503.10624},
  year={2025}
}