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Recovering 3D human body shape and pose from 2D images is a challenging taskdue to high complexity and flexibility of human body, and relatively less 3Dlabeled data. Previous methods addressing these issues typically rely onpredicting intermediate results such as body part segmentation, 2D/3D joints,silhouette mask to decompose the problem into multiple sub-tasks in order toutilize more 2D labels. Most previous works incorporated parametric body shapemodel in their methods and predict parameters in low-dimensional space torepresent human body. In this paper, we propose to directly regress the 3Dhuman mesh from a single color image using Convolutional Neural Network(CNN).We use an efficient representation of 3D human shape and pose which can bepredicted through an encoder-decoder neural network. The proposed methodachieves state-of-the-art performance on several 3D human body datasetsincluding Human3.6M, SURREAL and UP-3D with even faster running speed.

DenseBody: Directly Regressing Dense 3D Human Pose and Shape From a Single Color Image