We propose a bootstrapping framework to enhance human optical flow and pose.We show that, for videos involving humans in scenes, we can improve both theoptical flow and the pose estimation quality of humans by considering the twotasks at the same time. We enhance optical flow estimates by fine-tuning themto fit the human pose estimates and vice versa. In more detail, we optimize thepose and optical flow networks to, at inference time, agree with each other. Weshow that this results in state-of-the-art results on the Human 3.6M and 3DPoses in the Wild datasets, as well as a human-related subset of the Sinteldataset, both in terms of pose estimation accuracy and the optical flowaccuracy at human joint locations. Code available athttps://github.com/ubc-vision/bootstrapping-human-optical-flow-and-pose
使用pose来增强光流
@inproceedings{2210.15121,title={Bootstrapping Human Optical Flow and Pose},author={Arko, Aritro Roy and Little, James J. and Yi, Kwang Moo},year={2022},tags={human, optical-flow, pose-estimation},}