: Qualitative results of applying our 3D multi-person pose estimation framework to COCO dataset [20] which consists of in-the-wild images. Most of the previous 3D human pose estimation studies mainly focused on the root-relative 3D single-person pose estimation. In this study, we propose a general 3D multi-person pose estimation framework that takes into account all factors including human detection and 3D human root localization. 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 distanceaware 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 1,2 .
Moon et al. REF propose a root localization network to estimate the camera-centered coordinates of the human roots.
Camera Distance-Aware Top-Down Approach for 3D Multi-Person Pose Estimation From a Single RGB Image
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Computer Science