Leveraging Multi-View Weak Supervision for Occlusion-Aware Multi-Human Parsing
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908535343808512 |
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| author | Bragagnolo, Laura Terreran, Matteo Barcellona, Leonardo Ghidoni, Stefano |
| author_facet | Bragagnolo, Laura Terreran, Matteo Barcellona, Leonardo Ghidoni, Stefano |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_10093 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Leveraging Multi-View Weak Supervision for Occlusion-Aware Multi-Human Parsing Bragagnolo, Laura Terreran, Matteo Barcellona, Leonardo Ghidoni, Stefano Computer Vision and Pattern Recognition 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. |
| title | Leveraging Multi-View Weak Supervision for Occlusion-Aware Multi-Human Parsing |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.10093 |