Human body fitting, which aligns parametric body models such as SMPL to raw 3D point clouds of clothed humans, serves as a crucial first step for downstream tasks like animation and texturing. An effective fitting method should be both locally expressive-capturing fine details such as hands and facial features-and globally robust to handle real-world challenges, including clothing dynamics, pose variations, and noisy or partial inputs. Existing approaches typically excel in only one aspect, lacking an all-in-one solution.We upgrade ETCH to ETCH-X, which leverages a tightness-aware fitting paradigm to filter out clothing dynamics ("undress"), extends expressiveness with SMPL-X, and replaces explicit sparse markers (which are highly sensitive to partial data) with implicit dense correspondences ("dense fit") for more robust and fine-grained body fitting. Our disentangled "undress" and "dense fit" modular stages enable separate and scalable training on composable data sources, including diverse simulated garments (CLOTH3D), large-scale full-body motions (AMASS), and fine-grained hand gestures (InterHand2.6M), improving outfit generalization and pose robustness of both bodies and hands. Our approach achieves robust and expressive fitting across diverse clothing, poses, and levels of input completeness, delivering a substantial performance improvement over ETCH on both: 1) seen data, such as 4D-Dress (MPJPE-All, 33.0% ) and CAPE (V2V-Hands, 35.8% ), and 2) unseen data, such as BEDLAM2.0 (MPJPE-All, 80.8% ; V2V-All, 80.5% ). Code and models will be released at https://xiaobenli00.github.io/ETCH-X/.

翻译：人体拟合——将SMPL等参数化人体模型对齐到穿衣人体的原始三维点云——是动画、纹理等下游任务的关键第一步。有效的拟合方法需兼具局部表现力（捕捉手部、面部等精细细节）与全局鲁棒性，以应对服装动态、姿态变化及噪声或部分输入等实际挑战。现有方法通常仅擅长某一方面，缺乏一体化解决方案。我们将ETCH升级为ETCH-X，采用紧度感知拟合范式过滤服装动态（"脱衣"），通过SMPL-X扩展表现力，并以隐式密集对应（"密集拟合"）替代对部分数据高度敏感的显式稀疏标记，实现更鲁棒、更精细的人体拟合。解耦的"脱衣"与"密集拟合"模块化阶段支持基于可组合数据源的独立可扩展训练，这些数据源包括多样化模拟服装（CLOTH3D）、大规模全身运动（AMASS）及精细手部姿态（InterHand2.6M），从而提升身体与手部的着装泛化能力和姿态鲁棒性。我们的方法在多样化服装、姿态及输入完整度下实现了鲁棒且具表现力的拟合，相较于ETCH在以下场景均取得显著性能提升：1）已知数据，如4D-Dress（MPJPE-All提升33.0%）和CAPE（V2V-Hands提升35.8%）；2）未知数据，如BEDLAM2.0（MPJPE-All提升80.8%，V2V-All提升80.5%）。代码与模型将发布于https://xiaobenli00.github.io/ETCH-X/。