Human parsing is an important task in human-centric image understanding in computer vision and multimedia systems. However, most existing works on human parsing mainly tackle the single-person scenario, which deviates from real-world applications where multiple persons are present simultaneously with interaction and occlusion. To address such a challenging multi-human parsing problem, we introduce a novel multi-human parsing model named MH-Parser, which uses a graph-based generative adversarial model to address the challenges of close-person interaction and occlusion in multi-human parsing. To validate the effectiveness of the new model, we collect a new dataset named Multi-Human Parsing (MHP), which contains multiple persons with intensive person interaction and entanglement. Experiments on the new MHP dataset and existing datasets demonstrate that the proposed method is effective in addressing the multi-human parsing problem compared with existing solutions in the literature.

中文翻译：

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使用基于图的生成对抗模型进行多人解析

人体解析是计算机视觉和多媒体系统中以人为中心的图像理解的一项重要任务。然而，大多数现有的人体解析工作主要解决单人场景，这与现实世界的应用不同，在现实世界中，多人同时存在交互和遮挡。为了解决这样一个具有挑战性的多人类解析问题，我们引入了一种名为 MH-Parser 的新型多人类解析模型，它使用基于图的生成对抗模型来解决多人类解析中的近距离交互和遮挡的挑战. 为了验证新模型的有效性，我们收集了一个名为 Multi-Human Parsing (MHP) 的新数据集，其中包含多个具有密集的人交互和纠缠的人。