Abstract
Multi-human parsing is the task of segmenting human body parts while associating each part to the person it belongs to, combining instance-level and part-level information for fine-grained human understanding. In this work, we demonstrate that, while state-of-the-art approaches achieved notable results on public datasets, they struggle considerably in segmenting people with overlapping bodies. From the intuition that overlapping people may appear separated from a different point of view, we propose a novel training framework exploiting multi-view information to improve multi-human parsing models under occlusions. Our method integrates such knowledge during the training process, introducing a novel approach based on weak supervision on human instances and a multi-view consistency loss. Given the lack of suitable datasets in the literature, we propose a semi-automatic annotation strategy to generate human instance segmentation masks from multi-view RGB+D data and 3D human skeletons. The experiments demonstrate that the approach can achieve up to a 4.20\% relative improvement on human parsing over the baseline model in occlusion scenarios.
Abstract (translated)
多人解析任务是指分割人体部位，并将每个部分与所属的人关联起来，结合实例级别和部件级别的信息实现对人体的精细理解。在这项工作中，我们展示了尽管最先进的方法在公开数据集上取得了显著成果，但它们在分割重叠身体的人时遇到了重大挑战。基于这样的直觉：重叠的人从不同的视角看可能会显得分离，我们提出了一种新的训练框架，利用多视角信息来改善遮挡情况下的多人解析模型。我们的方法在训练过程中整合了这种知识，并引入了一种基于实例弱监督和多视图一致性损失的新方法。 由于文献中缺乏合适的数据集，我们还提出了一个半自动注释策略，用于从多视角RGB+D数据和3D人体骨架生成人类实例分割掩码。实验表明，在遮挡场景下，我们的方法相对于基线模型在人体解析上可以实现高达4.20%的相对改进。
URL
https://arxiv.org/abs/2509.10093
