ColorFM: An Optimization-to-Learning Framework for Color Transfer via Flow Matching

2026-07-08 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, quick

Summary

ColorFM is an optimization-to-learning framework designed to improve color transfer by aligning source and reference image color distributions while maintaining structural and semantic consistency. It addresses issues like inaccurate global mapping, semantic misalignment, and visual artifacts prevalent in existing methods. The framework comprises ColorFM-O, an instance-specific optimization scheme that fits velocity fields via Flow Matching with hierarchical color coupling and semantic priors, generating high-quality pseudo-supervision data. This data then trains ColorFM-L, an efficient feed-forward model that predicts flow parameters for bidirectional linearized transport. ColorFM-L demonstrates superior visual quality, structural fidelity, and semantic consistency compared to state-of-the-art methods, effectively combining optimization accuracy with inference speed.

Key takeaway

For computer vision engineers developing image processing pipelines, ColorFM offers a robust solution to overcome common color transfer artifacts and semantic misalignments. You should consider integrating Flow Matching-based optimization for generating high-quality training data, then training an efficient feed-forward model to achieve superior visual quality and structural fidelity in your applications.

Key insights

ColorFM connects online optimization to offline inference for precise color transfer via Flow Matching.

Principles

• Reformulate color transfer as pixel distribution transport.
• Use hierarchical color coupling with semantic priors.
• Combine optimization accuracy with feed-forward speed.

Method

ColorFM-O fits velocity fields via hierarchical color coupling and semantic priors, generating paired data. ColorFM-L trains on this data to predict flow parameters for bidirectional linearized transport.

In practice

• Apply Flow Matching to model pixel distribution transport.
• Generate pseudo-supervision data through instance-specific optimization.

Topics

• Color Transfer
• Flow Matching
• Computer Vision
• Image Processing
• Deep Learning
• Optimization
• Semantic Consistency

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.