ETCH-X: Robustify Expressive Body Fitting to Clothed Humans with Composable Synthetic Data

Human body fitting, which aligns parametric body models,such as SMPL, to raw 3D point clouds of clothed humans, serves as acrucial first step for downstream tasks like animation and texturing. Aneffective fitting method should be both locally expressive – capturingfine details such as hands and facial features – and globally robust tohandle real-world challenges, including clothing dynamics, pose variations,and noisy or partial inputs. Existing approaches typically excel in onlyone aspect, lacking an all-in-one solution. We upgrade ETCH to ETCH-X,which leverages a tightness-aware fitting paradigm to filter out clothingdynamics (“undress”), extends expressiveness with SMPL-X, and replacesexplicit sparse markers (which are highly sensitive to partial data) with im-plicit dense correspondences (“dense fit”) for more robust and fine-grainedbody fitting. Our disentangled “undress” and “dense fit” modular stages en-able separate and scalable training on composable data sources, includingdiverse simulated garments (CLOTH3D), large-scale full-body motions(AMASS), and fine-grained hand gestures (InterHand2.6M), improvingoutfit generalization and pose robustness of both bodies and hands. Ourapproach achieves robust and expressive fitting across diverse clothing,poses, and levels of input completeness, delivering a substantial perfor-mance improvement over ETCH on both 1) seen data, such as 4D-Dress(MPJPE-All, 33.0% ↓) and CAPE (V2V-Hands, 35.8% ↓), and 2) unseendata, such as BEDLAM2.0 (MPJPE-All, 80.8% ↓; V2V-All, 80.5% ↓).Code and models will be released at xiaobenli00.github.io/ETCH-X.