PifPaf: Composite Fields for Human Pose Estimation
We propose a new bottom up method for multi person 2D human pose estimation that is particularly well suited for urban mobility such as self driving cars and delivery robots. The new method, PifPaf, uses a Part Intensity Field (PIF) to localize body parts and a Part Association Field (PAF) to associate body parts with each other to form full human poses. Our method outperforms previous methods at low resolution and in crowded, cluttered and occluded scenes thanks to (i) our new composite field PAF encoding fine grained information and (ii) the choice of Laplace loss for regressions which incorporates a notion of uncertainty. Our architecture is based on a fully convolutional, single shot, box free design. We perform on par with the existing state of the art bottom up method on the standard COCO keypoint task and produce state of the art results on a modified COCO keypoint task for the transportation domain.
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)