Style Mixer: Semantic-aware Multi-Style Transfer Network

dc.contributor.authorHUANG, Zixuanen_US
dc.contributor.authorZHANG, Jinghuaien_US
dc.contributor.authorLIAO, Jingen_US
dc.contributor.editorLee, Jehee and Theobalt, Christian and Wetzstein, Gordonen_US
dc.description.abstractRecent neural style transfer frameworks have obtained astonishing visual quality and flexibility in Single-style Transfer (SST), but little attention has been paid to Multi-style Transfer (MST) which refers to simultaneously transferring multiple styles to the same image. Compared to SST, MST has the potential to create more diverse and visually pleasing stylization results. In this paper, we propose the first MST framework to automatically incorporate multiple styles into one result based on regional semantics. We first improve the existing SST backbone network by introducing a novel multi-level feature fusion module and a patch attention module to achieve better semantic correspondences and preserve richer style details. For MST, we designed a conceptually simple yet effective region-based style fusion module to insert into the backbone. It assigns corresponding styles to content regions based on semantic matching, and then seamlessly combines multiple styles together. Comprehensive evaluations demonstrate that our framework outperforms existing works of SST and MST.en_US
dc.description.sectionheadersImages and Learning
dc.description.seriesinformationComputer Graphics Forum
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectComputing methodologies
dc.subjectphotorealistic rendering
dc.subjectImage processing
dc.subjectNeural networks
dc.titleStyle Mixer: Semantic-aware Multi-Style Transfer Networken_US