ColorFM: An Optimization-to-Learning Framework for Color Transfer via Flow Matching
Color transfer aims to align the color distribution of a source image with that of a reference image while preserving structural and semantic consistency. However, existing methods often suffer from inaccurate global mapping, semantic misalignment, and visual artifacts. To address these issues, we propose ColorFM, an optimization-to-learning framework. ColorFM connects online optimization to offline inference by reformulating color transfer as the transport of pixel distributions along velocity fields via Flow Matching. Specifically, we introduce ColorFM-O, an instance-specific optimization scheme that fits the velocity field through hierarchical color coupling guided by semantic priors. ...
| Authors | Yuhang He, Kai Zhang, Xiaoming Li et al. |
|---|---|
| Primary category | cs.CV |
| Published | 8 July 2026 |
| Last updated at source | 8 July 2026 |
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- heyh97791/ColorFMDataset
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