TCFlow: Temporally Consistent Flow for Single-Reference Video Color Transfer

We study single-reference video color transfer: given a content video and one reference image, the goal is to generate a photorealistic and temporally stable video whose color palette matches the reference. This task is fundamentally constrained by temporal transport inconsistency, where minor frame-level distribution shifts caused by motion, occlusion, or lighting induce divergent color mappings and perceptible flickering. Existing approaches expose a stability-expressiveness trade-off: clip-level global transforms are temporally stable but limited in modeling complex reference-specific correspondences, whereas frame-wise methods are more expressive but often temporally incoherent. To address this limitation, we propose TCFlow, a unified meta-flow framework for robust and temporally consistent video color transfer. TCFlow predicts a clip-shared flow that transports the aggregated video-level content distribution toward a single reference palette. This clip-shared formulation inherently decouples global color structure from frame-specific variations, effectively suppressing color flickering. To ensure high-fidelity transfer, we introduce a color aggregator that synthesizes global color statistics across the video clip, enabling expressive and accurate color mapping without optical flow, dense motion supervision, or test-time optimization. Experiments show that TCFlow substantially suppresses color flickering and reduces frame-wise mapping variation. Beyond standard benchmarks, we validate TCFlow on a dataset of real-world media art exhibitions, where it consistently outperforms state-of-the-art methods in stability and photorealism. Further source code is provided in the supplementary material. Additional results, including qualitative comparisons and more video results, are available at https://tc-flow-iclr.vercel.app/