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arXiv · 2511.22330

Prompt-based Consistent Video Colorization

Silvia Dani·Tiberio Uricchio·Lorenzo Seidenari
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Abstract

Existing video colorization methods struggle with temporal flickering or demand extensive manual input. We propose a novel approach automating high-fidelity video colorization using rich semantic guidance derived from language and segmentation. We employ a language-conditioned diffusion model to colorize grayscale frames. Guidance is provided via automatically generated object masks and textual prompts; our primary automatic method uses a generic prompt, achieving state-of-the-art results without specific color input. Temporal stability is achieved by warping color information from previous frames using optical flow (RAFT); a correction step detects and fixes inconsistencies introduced by warping. Evaluations on standard benchmarks (DAVIS30, VIDEVO20) show our method achieves state-of-the-art performance in colorization accuracy (PSNR) and visual realism (Colorfulness, CDC), demonstrating the efficacy of automated prompt-based guidance for consistent video colorization.

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Silvia Dani, Tiberio Uricchio, Lorenzo Seidenari. 2025-11-27. Prompt-based Consistent Video Colorization. https://arxiv.org/abs/2511.22330

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