Fine-Grained Multi-human Parsing

Despite the noticeable progress in perceptual tasks like detection, instance segmentation and human parsing, computers still perform unsatisfactorily on visually understanding humans in crowded scenes, such as group behavior analysis, person re-identification, e-commerce, media editing, video surveillance, autonomous driving and virtual reality, etc. To perform well, models need to comprehensively perceive the semantic information and the differences between instances in a multi-human image, which is recently defined as the multi-human parsing task. In this paper, we first present a new large-scale database “Multi-human Parsing (MHP v2.0)” for algorithm development and evaluation to advance the research on understanding humans in crowded scenes. MHP v2.0 contains 25,403 elaborately annotated images with 58 fine-grained semantic category labels and 16 dense pose key point labels, involving 2–26 persons per image captured in real-world scenes from various viewpoints, poses, occlusion, interactions and background. We further propose a novel deep Nested Adversarial Network (NAN) model for multi-human parsing. NAN consists of three Generative Adversarial Network-like sub-nets, respectively performing semantic saliency prediction, instance-agnostic parsing and instance-aware clustering. These sub-nets form a nested structure and are carefully designed to learn jointly in an end-to-end way. NAN consistently outperforms existing state-of-the-art solutions on our MHP and several other datasets, including MHP v1.0, PASCAL-Person-Part and Buffy. NAN serves as a strong baseline to shed light on generic instance-level semantic part prediction and drive the future research on multi-human parsing. With the above innovations and contributions, we have organized the CVPR 2018 Workshop on Visual Understanding of Humans in Crowd Scene (VUHCS 2018) and the Fine-Grained Multi-human Parsing and Pose Estimation Challenge. These contributions together significantly benefit the community. Code and pre-trained models are available at https://github.com/ZhaoJ9014/Multi-Human-Parsing_MHP .