{"ID":2871737,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.10093","arxiv_id":"2509.10093","title":"Leveraging Multi-View Weak Supervision for Occlusion-Aware Multi-Human Parsing","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.","short_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 datase...","url_abs":"https://arxiv.org/abs/2509.10093","url_pdf":"https://arxiv.org/pdf/2509.10093v1","authors":"[\"Laura Bragagnolo\",\"Matteo Terreran\",\"Leonardo Barcellona\",\"Stefano Ghidoni\"]","published":"2025-09-12T09:36:23Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}