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. We evaluate our approach 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

本文旨在提高多人场景下的关节姿态估计技术。为此，我们在三个方面做出了贡献: 提出了改进的身体部位检测器、新颖的图像条件下的成对项以及一种递增优化策略。我们的方法在多人姿态估计任务中表现得更加优秀，并在单人姿态估计任务上展现出了竞争性的性能。

DeeperCut: 一种更深、更强、更快的多人姿态估计模型