WikiStyle+：一种用于艺术图像风格化的内容-风格表示解耦的多模态方法
WikiStyle+: A Multimodal Approach to Content-Style Representation Disentanglement for Artistic Image Stylization
AI总结：
针对现有艺术图像风格化方法依赖图像监督导致的单模态限制和解耦不彻底问题，本文构建WikiStyle+多模态数据集，提出解耦表示引导的扩散模型，实现更精细且符合参考风格艺术特征的风格化。
摘要：
艺术图像风格化旨在以目标风格渲染文本或图像提供的内容，其中内容与风格解耦是获得满意结果的关键。然而，当前内容与风格解耦方法主要依赖图像监督，导致两个问题：1）模型仅支持单模态的风格或内容输入；2）解耦不彻底，导致参考图像的内容泄露。为解决上述问题，本文提出一种用于艺术图像风格化的多模态内容-风格解耦方法。我们构建了WikiStyle+数据集，包含带风格和内容对应文本描述的艺术品。基于该多模态数据集，我们提出解耦表示引导的扩散模型：先通过Q-Formers学习解耦表示，再使用可学习的多步交叉注意力层注入预训练扩散模型。实验表明，该方法在多模态监督下实现参考图像内容与风格的彻底解耦，从而实现更精细、符合参考风格艺术特征的风格化。方法代码将在论文接受后公开。
英文摘要：
Artistic image stylization aims to render the content provided by text or image with the target style, where content and style decoupling is the key to achieve satisfactory results. However, current methods for content and style disentanglement primarily rely on image supervision, which leads to two problems: 1) models can only support one modality for style or content input;2) incomplete disentanglement resulting in content leakage from the reference image. To address the above issues, this paper proposes a multimodal approach to content-style disentanglement for artistic image stylization. We construct a \textit{WikiStyle+} dataset consists of artworks with corresponding textual descriptions for style and content. Based on the multimodal dataset, we propose a disentangled representations-guided diffusion model. The disentangled representations are first learned by Q-Formers and then injected into a pre-trained diffusion model using learnable multi-step cross-attention layers. Experimental results show that our method achieves a thorough disentanglement of content and style in reference images under multimodal supervision, thereby enabling more refined stylization that aligns with the artistic characteristics of the reference style. The code of our method will be available upon acceptance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
