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Estimating 3D human pose and shape from 2D images is a crucial yetchallenging task. While prior methods with model-based representations canperform reasonably well on whole-body images, they often fail when parts of thebody are occluded or outside the frame. Moreover, these results usually do notfaithfully capture the human silhouettes due to their limited representationpower of deformable models (e.g., representing only the naked body). Analternative approach is to estimate dense vertices of a predefined templatebody in the image space. Such representations are effective in localizingvertices within an image but cannot handle out-of-frame body parts. In thiswork, we learn dense human body estimation that is robust to partialobservations. We explicitly model the visibility of human joints and verticesin the x, y, and z axes separately. The visibility in x and y axes helpdistinguishing out-of-frame cases, and the visibility in depth axis correspondsto occlusions (either self-occlusions or occlusions by other objects). Weobtain pseudo ground-truths of visibility labels from dense UV correspondencesand train a neural network to predict visibility along with 3D coordinates. Weshow that visibility can serve as 1) an additional signal to resolve depthordering ambiguities of self-occluded vertices and 2) a regularization termwhen fitting a human body model to the predictions. Extensive experiments onmultiple 3D human datasets demonstrate that visibility modeling significantlyimproves the accuracy of human body estimation, especially for partial-bodycases. Our project page with code is at: https://github.com/chhankyao/visdb.

Learning Visibility for Robust Dense Human Body Estimation