Publications

@conference{nokey,

title = {Monocular Models are Strong Learners for Multi-View Human Mesh Recovery},

author = {Haoyu Xie; Shengkai Xu; Cheng Guo; Muhammad Usama Saleem; Wenhan Wu; Chen Chen; Ahmed Helmy; Pu Wang; Hongfei Xue},

url = {https://arxiv.org/abs/2603.20391},

year  = {2026},

date = {2026-09-08},

publisher = {European Conference on Computer Vision -- ECCV 2026},

abstract = {Multi-view human mesh recovery (HMR) is broadly deployed in diverse domains where high accuracy and strong generalization are essential. Existing approaches can be broadly grouped into geometry-based and learning-based methods. However, geometry-based methods (e.g., triangulation) rely on cumbersome camera calibration, while learning-based approaches often generalize poorly to unseen camera configurations due to the lack of multi-view training data, limiting their performance in real-world scenarios. To enable calibration-free reconstruction that generalizes to arbitrary camera setups, we propose a training-free framework that leverages pretrained single-view HMR models as strong priors, eliminating the need for multi-view training data. Our method first constructs a robust and consistent multi-view initialization from single-view predictions, and then refines it via test-time optimization guided by multi-view consistency and anatomical constraints. Extensive experiments demonstrate state-of-the-art performance on standard benchmarks, surpassing multi-view models trained with explicit multi-view supervision.},

keywords = {ECCV, Human Pose Estimation},

pubstate = {published},

tppubtype = {conference}

}

@conference{nokey_116,

title = {3D Human Pose Estimation with Spatial and Temporal Transformers},

author = {Ce Zheng and Sijie Zhu and Matias Mendieta and Taojiannan Yang and Chen Chen and Zhengming Ding},

url = {https://arxiv.org/pdf/2103.10455.pdf},

year  = {2021},

date = {2021-10-11},

urldate = {2021-10-11},

booktitle = {International Conference on Computer Vision},

keywords = {Action Recognition, Human Pose Estimation, ICCV, Pose},

pubstate = {published},

tppubtype = {conference}

}