(2016) DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model.
Abstract
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
| Item Type: | Conference or Workshop Item (A Paper) (Paper) |
|---|---|
| Conference: | ECCV European Conference on Computer Vision |
| Depositing User: | Sebastian Weisgerber |
| Date Deposited: | 22 Feb 2018 11:11 |
| Last Modified: | 22 Feb 2018 16:12 |
| URI: | https://publications.cispa.saarland/id/eprint/1847 |
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