Hierarchical Point Regression for Whole-Body Human Pose Estimation

HPRNet

HPRNet: Hierarchical Point Regression for Whole-Body Human Pose Estimation

Official PyTroch implementation of HPRNet.

HPRNet: Hierarchical Point Regression for Whole-Body Human Pose Estimation,
Nermin Samet, Emre Akbas,
Under review. (arXiv pre-print)

Highlights

• HPRNet is a bottom-up, one-stage and hierarchical keypoint regression method for whole-body pose estimation.
• HPRNet has the best performance among bottom-up methods for all the whole-body parts.
• HPRNet achieves SOTA performance for the face (76.0 AP) and hand (51.2 AP) keypoint estimation.
• Unlike two-stage methods, HPRNet predicts whole-body pose in a constant time independent of the number of people in an image.

COCO-WholeBody Keypoint Estimation Results

To finish reading, please visit source site