Researchers have introduced ColorFM, a novel framework for image color transfer that combines optimization and learning techniques. This approach reformulates color transfer as the movement of pixel distributions along velocity fields using Flow Matching. The framework includes ColorFM-O for instance-specific optimization and ColorFM-L, an efficient feed-forward model trained on generated data, which demonstrates superior performance in visual quality, structural fidelity, and semantic consistency compared to existing methods. AI
IMPACT This research could lead to more accurate and semantically consistent image color transfer, benefiting applications in image editing and content creation.
RANK_REASON The cluster describes a new research paper detailing a novel framework for image color transfer.
- alphaXiv
- arXiv
- CatalyzeX
- ColorFM
- ColorFM-L
- ColorFM-O
- DagsHub
- Flow Matching for Generative Modeling
- Gotit.pub
- Hugging Face
- Influence Flower
- ScienceCast
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