Mixture of Style Experts for Diverse Image Stylization
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/.
69ba7ade80a461b63f739c29
Shihao Zhu
claimed_verified
2026-03-19T13:01:36.991Z
66650de19f5a0b322999fe53
/avatars/0e299526084f1185346842f674c8383d.svg
Shihao Zhu
HH-LG
user
HH-LG
69ba7ade80a461b63f739c2a
Ziheng Ouyang
claimed_verified
2026-03-19T13:01:33.355Z
673c566071f89d461772aa36
/avatars/aff6378a57272e1c34bfcd827f39211a.svg
Ouyang Ziheng
ziheng1234
user
ziheng1234
69ba7ade80a461b63f739c2b
Yijia Kang
69ba7ade80a461b63f739c2c
Qilong Wang
69ba7ade80a461b63f739c2d
Mi Zhou
69ba7ade80a461b63f739c2e
Bo Li
69ba7ade80a461b63f739c2f
Ming-Ming Cheng
69ba7ade80a461b63f739c30
Qibin Hou
Mixture of Style Experts for Diverse Image Stylization
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/.
StyleExpert uses a Mixture of Experts architecture with a unified style encoder to handle diverse semantic levels in image stylization, outperforming existing methods in preserving semantic and material details.
Mixture of Experts style encoder latent space similarity-aware gating mechanism expert routing semantic awareness image stylization