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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
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2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2603.29924 |
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| _version_ | 1866908926853775360 |
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| author | Lu, Min He, Yuanfeng Chen, Anthony He, Jianhuang Wang, Pu Cohen-Or, Daniel Huang, Hui |
| author_facet | Lu, Min He, Yuanfeng Chen, Anthony He, Jianhuang Wang, Pu Cohen-Or, Daniel Huang, Hui |
| contents | Artistic styles often embed abstraction beyond surface appearance, involving deliberate reinterpretation of structure rather than mere changes in texture or color. Conventional style transfer methods typically preserve the input geometry and therefore struggle to capture this deeper abstraction behavior, especially for illustrative and nonphotorealistic styles. In this work, we introduce Abstraction in Style (AiS), a generative framework that separates structural abstraction from visual stylization. Given a target image and a small set of style exemplars, AiS first derives an intermediate abstraction proxy that reinterprets the target's structure in accordance with the abstraction logic exhibited by the style. The proxy captures semantic structure while relaxing geometric fidelity, enabling subsequent stylization to operate on an abstracted representation rather than the original image. In a second stage, the abstraction proxy is rendered to produce the final stylized output, preserving visual coherence with the reference style. Both stages are implemented using a shared image space analogy, enabling transformations to be learned from visual exemplars without explicit geometric supervision. By decoupling abstraction from appearance and treating abstraction as an explicit, transferable process, AiS supports a wider range of stylistic transformations, improves controllability, and enables more expressive stylization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_29924 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Abstraction in Style Lu, Min He, Yuanfeng Chen, Anthony He, Jianhuang Wang, Pu Cohen-Or, Daniel Huang, Hui Computer Vision and Pattern Recognition Artistic styles often embed abstraction beyond surface appearance, involving deliberate reinterpretation of structure rather than mere changes in texture or color. Conventional style transfer methods typically preserve the input geometry and therefore struggle to capture this deeper abstraction behavior, especially for illustrative and nonphotorealistic styles. In this work, we introduce Abstraction in Style (AiS), a generative framework that separates structural abstraction from visual stylization. Given a target image and a small set of style exemplars, AiS first derives an intermediate abstraction proxy that reinterprets the target's structure in accordance with the abstraction logic exhibited by the style. The proxy captures semantic structure while relaxing geometric fidelity, enabling subsequent stylization to operate on an abstracted representation rather than the original image. In a second stage, the abstraction proxy is rendered to produce the final stylized output, preserving visual coherence with the reference style. Both stages are implemented using a shared image space analogy, enabling transformations to be learned from visual exemplars without explicit geometric supervision. By decoupling abstraction from appearance and treating abstraction as an explicit, transferable process, AiS supports a wider range of stylistic transformations, improves controllability, and enables more expressive stylization. |
| title | Abstraction in Style |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.29924 |