Clair Obscur: an Illumination-Aware Method for Real-World Image Vectorization
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917332489601024 |
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| author | Lin, Xingyue Peng, Shuai Xie, Xiangyu Zhu, Jianhua Zhou, Yuxuan Gao, Liangcai |
| author_facet | Lin, Xingyue Peng, Shuai Xie, Xiangyu Zhu, Jianhua Zhou, Yuxuan Gao, Liangcai |
| contents | Image vectorization aims to convert raster images into editable, scalable vector representations while preserving visual fidelity. Existing vectorization methods struggle to represent complex real-world images, often producing fragmented shapes at the cost of semantic conciseness. In this paper, we propose COVec, an illumination-aware vectorization framework inspired by the Clair-Obscur principle of light-shade contrast. COVec is the first to introduce intrinsic image decomposition in the vector domain, separating an image into albedo, shade, and light layers in a unified vector representation. A semantic-guided initialization and two-stage optimization refine these layers with differentiable rendering. Experiments on various datasets demonstrate that COVec achieves higher visual fidelity and significantly improved editability compared to existing methods. The code will be released at https://github.com/decade-de/COVec. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_20034 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Clair Obscur: an Illumination-Aware Method for Real-World Image Vectorization Lin, Xingyue Peng, Shuai Xie, Xiangyu Zhu, Jianhua Zhou, Yuxuan Gao, Liangcai Computer Vision and Pattern Recognition Image vectorization aims to convert raster images into editable, scalable vector representations while preserving visual fidelity. Existing vectorization methods struggle to represent complex real-world images, often producing fragmented shapes at the cost of semantic conciseness. In this paper, we propose COVec, an illumination-aware vectorization framework inspired by the Clair-Obscur principle of light-shade contrast. COVec is the first to introduce intrinsic image decomposition in the vector domain, separating an image into albedo, shade, and light layers in a unified vector representation. A semantic-guided initialization and two-stage optimization refine these layers with differentiable rendering. Experiments on various datasets demonstrate that COVec achieves higher visual fidelity and significantly improved editability compared to existing methods. The code will be released at https://github.com/decade-de/COVec. |
| title | Clair Obscur: an Illumination-Aware Method for Real-World Image Vectorization |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.20034 |