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Deep, Robust and Single Shot 3D Multi-Person Human Pose Estimation from Monocular Images

Abdallah Benzine 1 Bertrand Luvison 1 Quoc Cuong Pham 1 Catherine Achard 2, 3, 4
1 LVIC - Laboratoire Vision et Ingénierie des Contenus
DIASI - Département Intelligence Ambiante et Systèmes Interactifs : DRT/LIST/DIASI
3 PIROS - Perception, Interaction, Robotique sociales
ISIR - Institut des Systèmes Intelligents et de Robotique
Abstract : In this paper, we propose a new single shot method for multi-person 3D pose estimation, from monocular RGB images. Our model jointly learns to locate the human joints in the image, to estimate their 3D coordinates and to group these predictions into full human skeletons. Our approach leverages and extends the Stacked Hourglass Network and its multi-scale feature learning to manage multi-person situations. Thus, we exploit the Occlusions Robust Pose Maps (ORPM) to fully describe several 3D human poses even in case of strong occlusions or cropping. Then, joint grouping and human pose estimation for an arbitrary number of people are performed using associative embedding. We evaluate our method on the challenging CMU Panoptic dataset, and demonstrate that it achieves better results than the state of the art.
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https://hal.sorbonne-universite.fr/hal-02459886
Contributor : Catherine Achard Connect in order to contact the contributor
Submitted on : Wednesday, January 29, 2020 - 4:39:07 PM
Last modification on : Friday, January 14, 2022 - 9:46:03 AM

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Abdallah Benzine, Bertrand Luvison, Quoc Cuong Pham, Catherine Achard. Deep, Robust and Single Shot 3D Multi-Person Human Pose Estimation from Monocular Images. 2019 IEEE International Conference on Image Processing (ICIP), The Institute of Electrical and Electronics Engineers Signal Processing Society, Sep 2019, Taipei, Taiwan. pp.584-588, ⟨10.1109/ICIP.2019.8803833⟩. ⟨hal-02459886⟩

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