Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

Understand

We present an approach to efficiently detect the 2D pose of multiple people in an image.

• The approach uses a nonparametric representation, which we refer to as Part Affinity Fields (PAFs), to learn to associate body parts with individuals in the image.
• The architecture encodes global context, allowing a greedy bottom-up parsing step that maintains high accuracy while achieving realtime performance, irrespective of the number of people in the image.
• The architecture is designed to jointly learn part locations and their association via two branches of the same sequential prediction process.