Abstraction in Style: Beyond Texture and Color
Min Lu, Yuanfeng He, Anthony Chen, Jianhuang He, Pu Wang, Daniel Cohen-Or, Hui Huang
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 non-photorealistic 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.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext ee6a6eb5-3c8c-4198-9312-57eeaf783b34Builds on16
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh et al.ICML 2021 · 47,906 citations
- LoRA: Low-Rank Adaptation of Large Language ModelsEdward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu et al.ICLR 2022 · 18,833 citations
- Adding Conditional Control to Text-to-Image Diffusion ModelsLvmin Zhang, Anyi Rao, Maneesh AgrawalaICCV 2023 · 6,759 citations
- CLIPasso: semantically-aware object sketchingYael Vinker, Ehsan Pajouheshgar, Jessica Y. Bo, Roman Christian Bachmann et al.SIGGRAPH 2022 · 219 citations
- TACo: Token-aware Cascade Contrastive Learning for Video-Text AlignmentJianwei Yang, Yonatan Bisk, Jianfeng GaoICCV 2021 · 159 citations
Related papers
- PNeSM: Arbitrary 3D Scene Stylization via Prompt-Based Neural Style MappingJiafu Chen, Wei Xing, Jiakai Sun, Tianyi Chu et al.AAAI 2024 · 2 citations
- Inversion-based Style Transfer with Diffusion ModelsYuxin Zhang, Nisha Huang, Fan Tang, Haibin Huang et al.CVPR 2023
- ArtBank: Artistic Style Transfer with Pre-trained Diffusion Model and Implicit Style Prompt BankZhanjie Zhang, Quanwei Zhang, Wei Xing, Guangyuan Li et al.AAAI 2024 · 32 citations
- Image-Guided Geometric Stylization of 3D MeshesChangwoon Choi, Hyunsoo Lee, Clément Jambon, Yael Vinker et al.CVPR 2026
- Supporting Expressive and Faithful Pictorial Visualization Design with Visual Style TransferYang Shi, Pei Liu, Siji Chen, Mengdi Sun et al.IEEE VIS 2022 · 40 citations
