StyleShot: A Snapshot on Any Style

1 Jul 2024  ·  Junyao Gao, Yanchen Liu, Yanan sun, Yinhao Tang, Yanhong Zeng, Kai Chen, Cairong Zhao ·

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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Datasets


Introduced in the Paper:

StyleBench StyleGallery

Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Style Transfer StyleBench StyleShot CLIP Score 0.660 # 1

Methods


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