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

TL;DR

This paper introduces an advanced multi-person pose estimation model that improves detection accuracy and speed by integrating better body part proposals, image-conditioned pairwise terms, and an efficient optimization strategy, outperforming existing methods.

Contribution

It presents a novel multi-person pose estimation framework with improved detectors, pairwise terms, and optimization, achieving state-of-the-art results and faster performance.

Findings

Significantly outperforms previous multi-person pose estimation methods.

Demonstrates competitive performance on single-person pose estimation.

Achieves faster inference with improved accuracy.

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