Precise Human Mesh Recovery (HMR) with in-the-wild data is a formidable challenge and is often hindered by depth ambiguities and reduced precision. Existing works resort to either pose priors or multi-modal data such as multi-view or point cloud information, though their methods often overlook the valuable scene-depth information inherently present in a single image. Moreover, achieving robust HMR for out-of-distribution (OOD) data is exceedingly challenging due to inherent variations in pose, shape and depth. Consequently, understanding the underlying distribution becomes a vital subproblem in modeling human forms. Motivated by the need for unambiguous and robust human modeling, we introduce Distribution and depth-aware human mesh recovery (D2A-HMR), an end-to-end transformer architecture meticulously designed to minimize the disparity between distributions and incorporate scene-depth leveraging prior depth information. Our approach demonstrates superior performance in handling OOD data in certain scenarios while consistently achieving competitive results against state-of-the-art HMR methods on controlled datasets.
@article{arxiv.2403.09063,
title = {Distribution and Depth-Aware Transformers for 3D Human Mesh Recovery},
author = {Jerrin Bright and Bavesh Balaji and Harish Prakash and Yuhao Chen and David A Clausi and John Zelek},
journal= {arXiv preprint arXiv:2403.09063},
year = {2024}
}
Comments
Submitted to 21st International Conference on Robots and Vision (CRV'24), Guelph, Ontario, Canada