Instance-level human analysis is common in real-life scenarios and has multiple manifestations, such as human part segmentation, dense pose estimation, human-object interactions, etc. Models need to distinguish different human instances in the image panel and learn rich features to represent the details of each instance. In this paper, we present an end-to-end pipeline for solving the instance-level human analysis, named Parsing R-CNN. It processes a set of human instances simultaneously through comprehensive considering the characteristics of region-based approach and the appearance of a human, thus allowing representing the details of instances. Parsing R-CNN is very flexible and efficient, which is applicable to many issues in human instance analysis. Our approach outperforms all state-of-the-art methods on CIHP (Crowd Instance-level Human Parsing), MHP v2.0 (Multi-Human Parsing) and DensePose-COCO datasets. Based on the proposed Parsing R-CNN, we reach the 1st place in the COCO 2018 Challenge DensePose Estimation task. Code and models are public available.

本文提出了一种名为Parsing R-CNN的用于解决实例级人体分析的端到端管道，其通过综合考虑区域方法的特征和人体的外观来同时处理一组人体实例，从而使其非常灵活高效，并在CIHP（Crowd Instance-level Human Parsing）、MHP v2.0（Multi-Human Parsing）和DensePose-COCO数据集上优于所有最先进的方法，也在COCO 2018 Challenge DensePose Estimation任务中获得了第一名。

用于实例级人体分析的Parsing R-CNN