Introduction Hi, I am Hiroto Honda, an AI R&D engineer at DeNA Co., Ltd. Japan. The research engineers in my computer vision (CV) team survey and discuss the latest CV papers every day. This time, we would like to share a part of our survey results on cutting-edge computer vision papers. Authors: Plot Hong, Toshihiro Hayashi and Hiroto Honda.
Scope of the survey
What is Human Recognition?
Papers CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark (CVPR2019 Oral) Deep High-Resolution Representation Learning for Human Pose Estimation Monocular Total Capture: Posing Face, Body, and Hands in the Wild (CVPR2019 Oral) Parsing R-CNN for Instance-Level Human Analysis 3D Hand Shape and Pose Estimation from a Single RGB Image (CVPR2019 Oral) Conclusion Quick Summary
Five arXiv papers regarding human and hand pose estimation, markerless motion capture, and body part segmentation are surveyed Using a multi-person pose estimation method on a region of interest is effective for crowded scenes. Keypoint localization accuracy can be improved by taking advantage of high resolution features. 3D human pose and mesh are estimated from a monocular RGB image. In addition to 3D pose estimation methods, deformable 3D mesh model, graph CNN, and synthetic data are utilized.