Abstraction in Style

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.

Figures (15)

• Figure 11: Generated examples: For the target image in each row, its style-agnostic hidden backbone is first generated. Then, five stylized outputs are produced, one for each reference style (columns). For each style, the system uses a trained A-VAT to generate an abstraction proxy (left grayscale image in each pair), which is then stylized via the corresponding S-VAT to create the final result (right image in the pair).
• Figure 12: Generated examples: Each column corresponds to a different target image. Each row corresponds to a different reference style (with one exemplar shown per style), which is applied to all targets in that row. The full set for each style comprises 5 to 10 exemplars, available in the supplementary material.
• Figure 13: Design examples mixing up the reference exemplars and the results generated by AiS: on the side are the original images from which the results are generated. The original images are ordered according to the result positions in the example, from top to bottom, left to right.
• Figure 14: Qualitative comparison with baseline methods: existing methods struggle with abstract styles and over-rigidly preserve input structure, producing unsatisfactory results. Our method excels at capturing nuanced artistic styles while maintaining natural structural variations. A larger version of this figure, including additional examples, is provided in the supplementary material.
• Figure 15: Reference exemplars and $2 \times 2$ image training samples for A-VAT (Backbone $\rightarrow$ Proxy) and S-VAT (Proxy $\rightarrow$ Output).