CanonCGT: Reference-Based Color Grading via Canonical Pivot Representation

• URL: http://arxiv.org/abs/2606.01638v1
• Date: Mon, 01 Jun 2026 03:42:20 GMT
• Title: CanonCGT: Reference-Based Color Grading via Canonical Pivot Representation
• Abstract summary: CanonCGT is a style-neutral intermediate representation for stable color mapping.<n>A dual-phase training scheme, DP-CGT, combines supervised preset learning with self-supervised refinement on unpaired photographs.
• Score: 16.555135705482485
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reference-based color grading aims to reproduce the tonal mood and lighting of a reference while preserving color harmony and scene structure. Existing photorealistic and filter-based methods often produce unstable tone mappings -- over-shifting or inconsistently retaining colors -- leading to unnatural results. We propose CanonCGT, a two-stage framework built on a canonical pivot -- a style-neutral intermediate representation for stable color mapping. The first stage canonicalizes the input by removing intrinsic tonal bias, and the second color-grades it to match the reference style. A dual-phase training scheme, DP-CGT, combines supervised preset learning with self-supervised refinement on unpaired photographs. CanonCGT delivers photorealistic and tonally consistent results across diverse datasets, surpassing state-of-the-art methods in stability and visual fidelity. Our codes are available at \href{https://github.com/Jinwon-Ko/CanonCGT}{https://github.com/Jinwon-Ko/CanonCGT}

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