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 this http URL: http://pose.mpi-inf.mpg.de/
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References in zbMATH (referenced in 3 articles )
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- Sven Kreiss, Lorenzo Bertoni, Alexandre Alahi: OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association (2021) arXiv
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- Saha, Monjoy; Chakraborty, Chandan: Her2Net: a deep framework for semantic segmentation and classification of cell membranes and nuclei in breast cancer evaluation (2018)