  Body orientation estimation provides crucial visual cues in many
applications, including robotics and autonomous driving. It is particularly
desirable when 3-D pose estimation is difficult to infer due to poor image
resolution, occlusion or indistinguishable body parts. We present COCO-MEBOW
(Monocular Estimation of Body Orientation in the Wild), a new large-scale
dataset for orientation estimation from a single in-the-wild image. The
body-orientation labels for around 130K human bodies within 55K images from the
COCO dataset have been collected using an efficient and high-precision
annotation pipeline. We also validated the benefits of the dataset. First, we
show that our dataset can substantially improve the performance and the
robustness of a human body orientation estimation model, the development of
which was previously limited by the scale and diversity of the available
training data. Additionally, we present a novel triple-source solution for 3-D
human pose estimation, where 3-D pose labels, 2-D pose labels, and our
body-orientation labels are all used in joint training. Our model significantly
outperforms state-of-the-art dual-source solutions for monocular 3-D human pose
estimation, where training only uses 3-D pose labels and 2-D pose labels. This
substantiates an important advantage of MEBOW for 3-D human pose estimation,
which is particularly appealing because the per-instance labeling cost for body
orientations is far less than that for 3-D poses. The work demonstrates high
potential of MEBOW in addressing real-world challenges involving understanding
human behaviors. Further information of this work is available at
https://chenyanwu.github.io/MEBOW/.
