Yuhang He¹, Kai Zhang^1,†, Xiaoming Li¹, Du Chen², Jian Yang¹

¹Nanjing University, China    ²VIVO BlueImage Lab, China
^†Corresponding author

ECCV 2026

• ColorFM: An Optimization-to-Learning Framework for Color Transfer via Flow Matching
  • Overview
  • Online Demos
  • Method
  • Quantitative Results
  • Image Color Transfer
  • Video Color Transfer
  • Citation

ColorFM is an optimization-to-learning framework for accurate and semantically consistent color transfer. It connects instance-specific optimization with efficient feed-forward inference through two complementary variants: ColorFM-O and ColorFM-L.

ColorFM formulates color transfer as transporting pixel distributions along velocity fields via Flow Matching. ColorFM-O optimizes an instance-specific velocity field with semantic guidance, while ColorFM-L learns from the generated pairs to provide efficient feed-forward inference.

Overview of the ColorFM-O and ColorFM-L frameworks.

The following table compares ColorFM with existing color transfer methods in terms of similarity, Lipschitz constant, and inference time. All results are evaluated at an image resolution of 512 x 512.

If you find this work useful, please cite:

@misc{he2026ColorFM,
      title={ColorFM: An Optimization-to-Learning Framework for Color Transfer via Flow Matching}, 
      author={Yuhang He and Kai Zhang and Xiaoming Li and Du Chen and Jian Yang},
      year={2026},
      eprint={2607.07119},
      url={https://arxiv.org/abs/2607.07119}, 
}