Abstract
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.
Abstract (translated)
艺术风格往往蕴含超越表层的抽象，涉及对结构的有意重构，而非简单的纹理或色彩变化。传统风格迁移方法通常保留输入图像的几何结构，因此难以捕捉这种深层的抽象特性，尤其对于插画与非真实感风格。本研究提出一种名为“风格中的抽象”（Abstraction in Style, AiS）的生成框架，该框架将结构抽象与视觉风格化分离。给定目标图像与少量风格范例后，AiS首先生成一个中间抽象代理，该代理依据风格所展现的抽象逻辑对目标结构进行重构。此代理在保留语义结构的同时放宽几何保真度，使后续风格化操作得以在抽象化表示而非原始图像上进行。第二阶段中，抽象代理被渲染以生成最终风格化输出，同时保持与参考风格的视觉一致性。两个阶段均通过共享图像空间类比实现，使变换能够从视觉范例中学习，而无需显式的几何监督。通过将抽象与外观解耦，并将抽象视为一个可显式转移的过程，AiS支持更广泛的风格转换，提升可控性，并实现更具表现力的风格化效果。
URL
https://arxiv.org/abs/2603.29924
