![]() Consequently, we follow the approach of SMPLify, which estimates 2D features and then optimizes model parameters to fit the features. ![]() Learning to regress the parameters of SMPL-X directly from images is challenging without paired images and 3D ground truth. To achieve this, we use thousands of 3D scans to train a new, unified, 3D model of the human body, SMPL-X, that extends SMPL with fully articulated hands and an expressive face. To facilitate the analysis of human actions, interactions and emotions, we compute a 3D model of human body pose, hand pose, and facial expression from a single monocular image. Expressive Body Capture: 3D Hands, Face, and Body from a Single Image Abstract
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