Distribution and Depth-Aware Transformers for 3D Human Mesh Recovery
Jerrin Bright Bavesh Balaji Harish Prakash Yuhao Chen David A Clausi John Zelek
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.