# ETCH
Generalizing Body Fitting to Clothed Humans via Equivariant Tightness

International Conference on Computer Vision 2025 (ICCV 2025)
⭐ Highlight Paper
Boqian Li1, Haiwen Feng2,3*, Zeyu Cai1, Michael J. Black2, Yuliang Xiu1,2†
1Westlake University 2Max Planck Institute for Intelligent Systems 3UC Berkeley
*Project Lead †Corresponding Author
Code arXiv Twitter Video

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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

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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 fequiv , along with the tightness magnitudes B, labels L, and confidences C via invariant features finv . 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.

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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}
    }