Recent Advances in Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective

Estimation of the human pose from a monocular camera has been an emerging research topic in the computer vision community with many applications. Recently, benefited from the deep learning technologies, a significant amount of research efforts have greatly advanced the monocular human pose estimation both in 2D and 3D areas. Although there have been some works to summarize the different approaches, it still remains challenging for researchers to have an in-depth view of how these approaches work. In this paper, we provide a comprehensive and holistic 2D-to-3D perspective to tackle this problem. We categorize the mainstream and milestone approaches since the year 2014 under unified frameworks. By systematically summarizing the differences and connections between these approaches, we further analyze the solutions for challenging cases, such as the lack of data, the inherent ambiguity between 2D and 3D, and the complex multi-person scenarios. We also summarize the pose representation styles, benchmarks, evaluation metrics, and the quantitative performance of popular approaches. Finally, we discuss the challenges and give deep thinking of promising directions for future research. We believe this survey will provide the readers with a deep and insightful understanding of monocular human pose estimation.

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[138]  Dacheng Tao,et al.  A Coarse-Fine Network for Keypoint Localization , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[139]  Andrew Zisserman,et al.  Recurrent Human Pose Estimation , 2016, 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017).

[140]  Xiu-Shen Wei,et al.  Adversarial PoseNet: A Structure-Aware Convolutional Network for Human Pose Estimation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

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[151]  Dongdong Yu,et al.  Multi-Person Pose Estimation With Enhanced Channel-Wise and Spatial Information , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[152]  Kostas Daniilidis,et al.  Convolutional Mesh Regression for Single-Image Human Shape Reconstruction , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

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[156]  Ying Wu,et al.  Does Learning Specific Features for Related Parts Help Human Pose Estimation? , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

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[158]  Yaser Sheikh,et al.  OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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[160]  Alan L. Yuille,et al.  OriNet: A Fully Convolutional Network for 3D Human Pose Estimation , 2018, BMVC.

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[163]  Georgios Tzimiropoulos,et al.  Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment with Limited Resources , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[164]  Andrea Palazzi,et al.  Learning to Detect and Track Visible and Occluded Body Joints in a Virtual World , 2018, ECCV.

[165]  Jia Deng,et al.  Stacked Hourglass Networks for Human Pose Estimation , 2016, ECCV.

[166]  Yichen Wei,et al.  Integral Human Pose Regression , 2017, ECCV.

[167]  Haoyu Wang,et al.  Pose Flow: Efficient Online Pose Tracking , 2018, BMVC.

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