Instance-level Human Parsing via Part Grouping Network

TL;DR

This paper introduces a detection-free Part Grouping Network (PGN) that efficiently performs instance-level human parsing in a single pass by jointly learning semantic part segmentation and instance-aware edge detection, outperforming existing methods.

Contribution

The novel PGN framework jointly learns semantic part segmentation and edge detection for multi-person parsing without detection, improving accuracy and efficiency.

Findings

PGN outperforms state-of-the-art methods on PASCAL-Person-Part.

PGN achieves superior results on the large-scale CIHP dataset.

The approach simplifies multi-person parsing by avoiding detection steps.

Abstract

Instance-level human parsing towards real-world human analysis scenarios is still under-explored due to the absence of sufficient data resources and technical difficulty in parsing multiple instances in a single pass. Several related works all follow the "parsing-by-detection" pipeline that heavily relies on separately trained detection models to localize instances and then performs human parsing for each instance sequentially. Nonetheless, two discrepant optimization targets of detection and parsing lead to suboptimal representation learning and error accumulation for final results. In this work, we make the first attempt to explore a detection-free Part Grouping Network (PGN) for efficiently parsing multiple people in an image in a single pass. Our PGN reformulates instance-level human parsing as two twinned sub-tasks that can be jointly learned and mutually refined via a unified network: 1) semantic part segmentation for assigning each pixel as a human part (e.g., face, arms); 2) instance-aware edge detection to group semantic parts into distinct person instances. Thus the shared intermediate representation would be endowed with capabilities in both characterizing fine-grained parts and inferring instance belongings of each part. Finally, a simple instance partition process is employed to get final results during inference. We conducted experiments on PASCAL-Person-Part dataset and our PGN outperforms all state-of-the-art methods. Furthermore, we show its superiority on a newly collected multi-person parsing dataset (CIHP) including 38,280 diverse images, which is the largest dataset so far and can facilitate more advanced human analysis. The CIHP benchmark and our source code are available at http://sysu-hcp.net/lip/.