StyleShot: A Snapshot on Any Style
This addresses the need for efficient and versatile style transfer in image processing, though it appears incremental as it builds on existing methods with specific enhancements.
The paper tackles the problem of generalized style transfer without test-time tuning by developing a style-aware encoder and a style dataset, achieving superior performance across various styles compared to state-of-the-art methods.
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/.