DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model

Code

• github.com/gyaansastra/DeepLab
  tf★ 1
• github.com/orkqueen/depplabseongil
  tf★ 1
• github.com/PJunhyuk/exercise-pose-analyzer
  tf★ 0
• github.com/yttrilab/b-soid
  none★ 0
• github.com/Ayaanesmail/Test.-
  tf★ 0
• github.com/eldar/deepcut
  none★ 0
• github.com/eho-tacc/DeepLabCut
  tf★ 0
• github.com/srini2dl/DogPoseEstimation
  tf★ 0
• github.com/eldar/deepcut-cnn
  none★ 0
• github.com/gsoykan/deepercut-replication
  tf★ 0

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. 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

Tasks

Benchmark Results

Reproductions