  Reconstructing 3D human shape and pose from monocular images is challenging
despite the promising results achieved by the most recent learning-based
methods. The commonly occurred misalignment comes from the facts that the
mapping from images to the model space is highly non-linear and the
rotation-based pose representation of body models is prone to result in the
drift of joint positions. In this work, we investigate learning 3D human shape
and pose from dense correspondences of body parts and propose a
Decompose-and-aggregate Network (DaNet) to address these issues. DaNet adopts
the dense correspondence maps, which densely build a bridge between 2D pixels
and 3D vertices, as intermediate representations to facilitate the learning of
2D-to-3D mapping. The prediction modules of DaNet are decomposed into one
global stream and multiple local streams to enable global and fine-grained
perceptions for the shape and pose predictions, respectively. Messages from
local streams are further aggregated to enhance the robust prediction of the
rotation-based poses, where a position-aided rotation feature refinement
strategy is proposed to exploit spatial relationships between body joints.
Moreover, a Part-based Dropout (PartDrop) strategy is introduced to drop out
dense information from intermediate representations during training,
encouraging the network to focus on more complementary body parts as well as
neighboring position features. The efficacy of the proposed method is validated
on both indoor and real-world datasets including Human3.6M, UP3D, COCO, and
3DPW, showing that our method could significantly improve the reconstruction
performance in comparison with previous state-of-the-art methods. Our code is
publicly available at https://hongwenzhang.github.io/dense2mesh .
