Video Color Grading via Look-Up Table Generation

TL;DR

This paper introduces a novel reference-based video color grading framework that uses a diffusion model to generate look-up tables, enabling artistic color adjustments aligned with reference scenes and user preferences, with fast inference and high-quality results.

Contribution

We propose a diffusion model-based LUT generation method for video color grading that preserves details and incorporates user preferences through text prompts.

Findings

Effective color grading aligned with reference scenes demonstrated.

Fast inference achieved with LUT-based approach.

User studies confirm high-quality, customizable results.

Abstract

Different from color correction and transfer, color grading involves adjusting colors for artistic or storytelling purposes in a video, which is used to establish a specific look or mood. However, due to the complexity of the process and the need for specialized editing skills, video color grading remains primarily the domain of professional colorists. In this paper, we present a reference-based video color grading framework. Our key idea is explicitly generating a look-up table (LUT) for color attribute alignment between reference scenes and input video via a diffusion model. As a training objective, we enforce that high-level features of the reference scenes like look, mood, and emotion should be similar to that of the input video. Our LUT-based approach allows for color grading without any loss of structural details in the whole video frames as well as achieving fast inference. We further build a pipeline to incorporate a user-preference via text prompts for low-level feature enhancement such as contrast and brightness, etc. Experimental results, including extensive user studies, demonstrate the effectiveness of our approach for video color grading. Codes are publicly available at https://github.com/seunghyuns98/VideoColorGrading.