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