  Human pose and shape are two important components of 2D human body. However,
how to efficiently represent both of them in images is still an open question.
In this paper, we propose the Triplet Representation for Body (TRB) -- a
compact 2D human body representation, with skeleton keypoints capturing human
pose information and contour keypoints containing human shape information. TRB
not only preserves the flexibility of skeleton keypoint representation, but
also contains rich pose and human shape information. Therefore, it promises
broader application areas, such as human shape editing and conditional image
generation. We further introduce the challenging problem of TRB estimation,
where joint learning of human pose and shape is required. We construct several
large-scale TRB estimation datasets, based on popular 2D pose datasets: LSP,
MPII, COCO. To effectively solve TRB estimation, we propose a two-branch
network (TRB-net) with three novel techniques, namely X-structure (Xs),
Directional Convolution (DC) and Pairwise Mapping (PM), to enforce multi-level
message passing for joint feature learning. We evaluate our proposed TRB-net
and several leading approaches on our proposed TRB datasets, and demonstrate
the superiority of our method through extensive evaluations.
