MEBOW: Monocular Estimation of Body Orientation In the Wild

Chenyan Wu (1,2), Yukun Chen (1), Jiajia Luo (2), Che-Chun Su (2), Anuja Dawane (2),
Bikramjot Hanzra (2), Zhuo Deng (2), Bilan Liu (2), James Z. Wang (1), Cheng-hao Kuo (2)
(1) The Pennsylvania State University, University Park
(2) Amazon Lab126

Abstract:

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/ .


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Citation: Chenyan Wu, Yukun Chen, Jiajia Luo, Che-Chun Su, Anuja Dawane, Bikramjot Hanzra, Zhuo Deng, Bilan Liu, James Z. Wang and Cheng-hao Kuo, ``MEBOW: Monocular Estimation of Body Orientation In the Wild,'' Proceedings of the International Conference on Computer Vision and Pattern Recognition, pp. -, 2020.

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Last Modified: March 29, 2020.
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