Publications

2026

1. ETCH-X: Robustify Expressive Body Fitting to Clothed Humans with Composable Datasets

   Xiaoben Li , Jingyi Wu, Zeyu Cai, Siyuan Yu , Boqian Li, and Yuliang Xiu

   In The European Conference on Computer Vision (ECCV) , 2026

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2. OmniFit: Multi-modal 3D Body Fitting via Scale-agnostic Dense Landmark Prediction

   Zeyu Cai, Yuliang Xiu, Renke Wang, Zhijing Shao, Xiaoben Li, Siyuan Yu, Chao Xu, Yang Liu, Baigui Sun , Jian Yang, and Zhenyu Zhang

   In The European Conference on Computer Vision (ECCV) , 2026

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2024

1. Human Mesh Recovery from Arbitrary Multi-view Images

   Xiaoben Li, Mancheng Meng, Ziyan Wu, Terrence Chen, Fan Yang, and Dinggang Shen

   arXiv preprint, 2024

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   Human mesh recovery from arbitrary multi-view images involves two characteristics: the arbitrary camera poses and arbitrary number of camera views. Because of the variability, designing a unified framework to tackle this task is challenging. The challenges can be summarized as the dilemma of being able to simultaneously estimate arbitrary camera poses and recover human mesh from arbitrary multi-view images while maintaining flexibility. To solve this dilemma, we propose a divide and conquer framework for Unified Human Mesh Recovery (U-HMR) from arbitrary multi-view images. In particular, U-HMR consists of a decoupled structure and two main components: camera and body decoupling (CBD), camera pose estimation (CPE), and arbitrary view fusion (AVF). As camera poses and human body mesh are independent of each other, CBD splits the estimation of them into two sub-tasks for two individual sub-networks (i.e., CPE and AVF) to handle respectively, thus the two sub-tasks are disentangled. In CPE, since each camera pose is unrelated to the others, we adopt a shared MLP to process all views in a parallel way. In AVF, in order to fuse multi-view information and make the fusion operation independent of the number of views, we introduce a transformer decoder with a SMPL parameters query token to extract cross-view features for mesh recovery. To demonstrate the efficacy and flexibility of the proposed framework and effect of each component, we conduct extensive experiments on three public datasets: Human3.6M, MPI-INF-3DHP, and TotalCapture.

2. Self-learning Canonical Space for Multi-view 3D Human Pose Estimation

   Xiaoben Li, Mancheng Meng, Ziyan Wu, Terrence Chen, Fan Yang, and Dinggang Shen

   arXiv preprint, 2024

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   Multi-view 3D human pose estimation is naturally superior to single view one, benefiting from more comprehensive information provided by images of multiple views. The information includes camera poses, 2D/3D human poses, and 3D geometry. However, the accurate annotation of these information is hard to obtain, making it challenging to predict accurate 3D human pose from multi-view images. To deal with this issue, we propose a fully self-supervised framework, named cascaded multi-view aggregating network (CMANet), to construct a canonical parameter space to holistically integrate and exploit multi-view information. In our framework, the multi-view information is grouped into two categories: 1) intra-view information (i.e., camera pose, projected 2D human pose, view-dependent 3D human pose), 2) inter-view information (i.e., cross-view complement and 3D geometry constraint). Accordingly, CMANet consists of two components: intra-view module (IRV) and inter-view module (IEV). IRV is used for extracting initial camera pose and 3D human pose of each view; IEV is to fuse complementary pose information and cross-view 3D geometry for a final 3D human pose. To facilitate the aggregation of the intra- and inter-view, we define a canonical parameter space, depicted by per-view camera pose and human pose and shape parameters (θand β) of SMPL model, and propose a two-stage learning procedure. At first stage, IRV learns to estimate camera pose and view-dependent 3D human pose supervised by confident output of an off-the-shelf 2D keypoint detector. At second stage, IRV is frozen and IEV further refines the camera pose and optimizes the 3D human pose by implicitly encoding the cross-view complement and 3D geometry constraint, achieved by jointly fitting predicted multi-view 2D keypoints. The proposed framework, modules, and learning strategy are demonstrated to be effective by comprehensive experiments and CMANet is superior to state-of-the-art methods in extensive quantitative and qualitative analysis.