DeeperCut: A Deeper, Stronger, and Faster Multi person Pose Estimation Model
The goal of this paper is to advance the state of the art of articulated pose estimation in scenes with multiple people. To that end we contribute on three fronts. We propose (1) improved body part detectors that generate effective bottom up proposals for body parts; (2) novel image conditioned pairwise terms that allow to assemble the proposals into a variable number of consistent body part configurations; and (3) an incremental optimization strategy that explores the search space more efficiently thus leading both to better performance and significant speed up factors. Evaluation is done on two single person and two multi person pose estimation benchmarks. The proposed approach significantly outperforms best known multi person pose estimation results while demonstrating competitive performance on the task of single person pose estimation (Models and code available at http:/pose.mpi inf.mpg.de)
ECCV