Distribution and Depth-Aware Transformers for 3D Human Mesh Recovery
<|reference_start|>Distribution and Depth-Aware Transformers for 3D Human Mesh Recovery: 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.<|reference_end|>
@article{bright2024distribution,
    title={Distribution and Depth-Aware Transformers for 3D Human Mesh Recovery},
    author={Jerrin Bright, Bavesh Balaji, Harish Prakash, Yuhao Chen, David A
  Clausi and John Zelek},
    journal={arXiv preprint arXiv:2403.09063},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.09063},
    primaryClass={cs.CV cs.AI}
}