Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image

ICCV 2019  ·  Gyeongsik Moon, Ju Yong Chang, Kyoung Mu Lee ·

Although significant improvement has been achieved recently in 3D human pose estimation, most of the previous methods only treat a single-person case. In this work, we firstly propose a fully learning-based, camera distance-aware top-down approach for 3D multi-person pose estimation from a single RGB image. The pipeline of the proposed system consists of human detection, absolute 3D human root localization, and root-relative 3D single-person pose estimation modules. Our system achieves comparable results with the state-of-the-art 3D single-person pose estimation models without any groundtruth information and significantly outperforms previous 3D multi-person pose estimation methods on publicly available datasets. The code is available in https://github.com/mks0601/3DMPPE_ROOTNET_RELEASE , https://github.com/mks0601/3DMPPE_POSENET_RELEASE.

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Results from the Paper


 Ranked #1 on Monocular 3D Human Pose Estimation on Human3.6M (Use Video Sequence metric)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
3D Human Pose Estimation 3D Poses in the Wild Challenge RootNet MPJPE 84.28 # 4
MPJAE 21.25 # 2
Root Joint Localization Human3.6M 3DMPPE_ROOTNET MRPE 120.0 # 2
3D Absolute Human Pose Estimation Human3.6M RootNet MRPE 120.0 # 2
3D Human Pose Estimation Human3.6M PoseNet (GTi) Average MPJPE (mm) 53.3 # 221
Monocular 3D Human Pose Estimation Human3.6M Moon et. al. Use Video Sequence No # 1
Frames Needed 1 # 1
Need Ground Truth 2D Pose No # 1
3D Human Pose Estimation Human3.6M PoseNet Average MPJPE (mm) 54.4 # 229
3D Multi-Person Pose Estimation (root-relative) MuPoTS-3D 3DMPPE_POSENET 3DPCK 81.8 # 14
3D Multi-Person Pose Estimation (absolute) MuPoTS-3D 3DMPPE_POSENET 3DPCK 31.5 # 13

Methods


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