Learning to Refine Human Pose Estimation

Understand

Multi-person pose estimation in images and videos is an important yet challenging task with many applications.

• Despite the large improvements in human pose estimation enabled by the development of convolutional neural networks, there still exist a lot of difficult cases where even the state-of-the-art models fail to correctly localize all body joints.
• This motivates the need for an additional refinement step that addresses these challenging cases and can be easily applied on top of any existing method.
• In this work, we introduce a pose refinement network (PoseRefiner) which takes as input both the image and a given pose estimate and learns to directly predict a refined pose by jointly reasoning about the input-output space.