风格专家混合实现多样化图像风格化

摘要

基于扩散模型的风格化技术已取得显著进展，但现有方法多局限于色彩驱动的转换，未能兼顾复杂语义与材质细节。本文提出StyleExpert——一种基于专家混合模型（MoE）的语义感知框架。该框架采用通过大规模内容-风格-风格化三元组数据集训练的统一风格编码器，将多样风格嵌入至统一潜在空间。该嵌入向量随后用于驱动相似性感知门控机制，动态地将风格分配至MoE架构中的特定专家。借助MoE架构，我们的方法能娴熟处理从浅层纹理到深层语义的多层级风格。大量实验表明，StyleExpert在保持语义完整性与材质细节方面优于现有方法，并对未见风格具备良好泛化能力。代码及收集图像详见项目页面：https://hh-lg.github.io/StyleExpert-Page/。

English

Diffusion-based stylization has advanced significantly, yet existing methods are limited to color-driven transformations, neglecting complex semantics and material details.We introduce StyleExpert, a semantic-aware framework based on the Mixture of Experts (MoE). Our framework employs a unified style encoder, trained on our large-scale dataset of content-style-stylized triplets, to embed diverse styles into a consistent latent space. This embedding is then used to condition a similarity-aware gating mechanism, which dynamically routes styles to specialized experts within the MoE architecture. Leveraging this MoE architecture, our method adeptly handles diverse styles spanning multiple semantic levels, from shallow textures to deep semantics. Extensive experiments show that StyleExpert outperforms existing approaches in preserving semantics and material details, while generalizing to unseen styles. Our code and collected images are available at the project page: https://hh-lg.github.io/StyleExpert-Page/.

风格专家混合实现多样化图像风格化

Mixture of Style Experts for Diverse Image Stylization

摘要

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