Style-CCL uses curriculum continual learning on a million-scale synthetic dataset with a dual-branch SC-DiT to achieve state-of-the-art content-preserving style transfer.
Preserving Color in Neural Artistic Style Transfer
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abstract
This note presents an extension to the neural artistic style transfer algorithm (Gatys et al.). The original algorithm transforms an image to have the style of another given image. For example, a photograph can be transformed to have the style of a famous painting. Here we address a potential shortcoming of the original method: the algorithm transfers the colors of the original painting, which can alter the appearance of the scene in undesirable ways. We describe simple linear methods for transferring style while preserving colors.
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cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Style-CCL: Content-Preserving Style Transfer via Curriculum Continual Learning
Style-CCL uses curriculum continual learning on a million-scale synthetic dataset with a dual-branch SC-DiT to achieve state-of-the-art content-preserving style transfer.