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PandaNet: Anchor-Based Single-Shot Multi-Person 3D Pose Estimation

Résumé : Recently, several deep learning models have been pro-posed for 3D human pose estimation. Nevertheless, mostof these approaches only focus on the single-person caseor estimate 3D pose of a few people at high resolution.Furthermore, many applications such as autonomous driv-ing or crowd analysis require pose estimation of a largenumber of people possibly at low-resolution. In this work,we present PandaNet (Pose estimAtioN and DectectionAnchor-based Network), a new single-shot, anchor-basedand multi-person 3D pose estimation approach. The pro-posed model performs bounding box detection and, for eachdetected person, 2D and 3D pose regression into a singleforward pass. It does not need any post-processing to re-group joints since the network predicts a full 3D pose foreach bounding box and allows the pose estimation of a pos-sibly large number of people at low resolution. To managepeople overlapping, we introduce a Pose-Aware Anchor Se-lection strategy. Moreover, as imbalance exists between dif-ferent people sizes in the image, and joints coordinates have ifferent uncertainties depending on these sizes, we pro-pose a method to automatically optimize weights associatedto different people scales and joints for efficient training.PandaNet surpasses previous single-shot methods on sev-eral challenging datasets: a multi-person urban virtual butvery realistic dataset (JTA Dataset), and two real world 3Dmulti-person datasets (CMU Panoptic and MuPoTS-3D).
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https://hal.sorbonne-universite.fr/hal-02926220
Contributor : Catherine Achard <>
Submitted on : Monday, August 31, 2020 - 3:18:57 PM
Last modification on : Friday, June 25, 2021 - 9:48:05 AM
Long-term archiving on: : Tuesday, December 1, 2020 - 12:39:27 PM

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Abdallah Benzine, Florian Chabot, Bertrand Luvison, Quoc Cuong Pham, Catherine Achard. PandaNet: Anchor-Based Single-Shot Multi-Person 3D Pose Estimation. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2020, Seattle, United States. pp.6855-6864, ⟨10.1109/CVPR42600.2020.00689⟩. ⟨hal-02926220⟩

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