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StyleShot: A Snapshot on Any Style

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arxiv 2407.01414 v2 pith:5Z3RORPZ submitted 2024-07-01 cs.CV

StyleShot: A Snapshot on Any Style

Junyao Gao , Yanchen Liu , Yanan Sun , Yinhao Tang , Yanhong Zeng , Kai Chen , Cairong Zhao This is my paper
classification cs.CV
keywords stylestyleshotencoderstylesrepresentationstyle-awarestylegallerytest-time
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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In this paper, we show that, a good style representation is crucial and sufficient for generalized style transfer without test-time tuning. We achieve this through constructing a style-aware encoder and a well-organized style dataset called StyleGallery. With dedicated design for style learning, this style-aware encoder is trained to extract expressive style representation with decoupling training strategy, and StyleGallery enables the generalization ability. We further employ a content-fusion encoder to enhance image-driven style transfer. We highlight that, our approach, named StyleShot, is simple yet effective in mimicking various desired styles, i.e., 3D, flat, abstract or even fine-grained styles, without test-time tuning. Rigorous experiments validate that, StyleShot achieves superior performance across a wide range of styles compared to existing state-of-the-art methods. The project page is available at: https://styleshot.github.io/.

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