Hi-Light: A Path to high-fidelity, high-resolution video relighting with a Novel Evaluation Paradigm

2026-01-30

AI Summary

This work addresses key challenges in video relighting, including the lack of evaluation metrics, temporal flickering, and detail degradation. The authors propose a training-free, high-fidelity, high-resolution video relighting framework that leverages a photometric prior-guided diffusion model for precise illumination control. Temporal flickering is effectively suppressed through a flow-based hybrid motion-adaptive lighting smoothing filter, while high-frequency details are preserved by fusing texture information in the LAB color space. To systematically evaluate lighting consistency, the study introduces the first photometric stability scoring metric for relit videos. Experimental results demonstrate that the proposed method significantly outperforms existing approaches in both qualitative and quantitative assessments, achieving superior lighting stability and rich textural detail.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Video relighting offers immense creative potential and commercial value but is hindered by challenges, including the absence of an adequate evaluation metric, severe light flickering, and the degradation of fine-grained details during editing. To overcome these challenges, we introduce Hi-Light, a novel, training-free framework for high-fidelity, high-resolution, robust video relighting. Our approach introduces three technical innovations: lightness prior anchored guided relighting diffusion that stabilises intermediate relit video, a Hybrid Motion-Adaptive Lighting Smoothing Filter that leverages optical flow to ensure temporal stability without introducing motion blur, and a LAB-based Detail Fusion module that preserves high-frequency detail information from the original video. Furthermore, to address the critical gap in evaluation, we propose the Light Stability Score, the first quantitative metric designed to specifically measure lighting consistency. Extensive experiments demonstrate that Hi-Light significantly outperforms state-of-the-art methods in both qualitative and quantitative comparisons, producing stable, highly detailed relit videos.

Problem

• video relighting
• light flickering
• detail degradation
• evaluation metric
• temporal stability

Innovation

• video relighting
• training-free framework
• temporal stability
• detail preservation
• Light Stability Score