LiveLight: Real-time Streaming Video Relighting with Interactive Control

2026-08-03Computer Vision and Pattern Recognition

Computer Vision and Pattern Recognition
AI summary

The authors introduce LiveLight, a new method using diffusion models to change lighting in streaming videos in real-time while allowing users to control 3D lighting interactively. They solve major challenges by designing a special adapter that adds detailed 3D lighting info, using a geometry-based feedback system to keep high image quality at fast speeds, and creating a method to smoothly update video frames over time. Their tests show LiveLight works better than previous methods in quality, stability, and user control, and they plan to share their resources for others to use. This advances how lighting effects can be applied live in videos.

diffusion modelvideo relighting3D lighting controlMulti-Plane Light Irradiance (MPLI)function evaluations (NFE)geometry-guided feedbackdenoising laddertemporal coherencereal-time streaminginteractive control
Authors
Yue Ma, Jiangming Wang, Yucheng Wang, Xilai Wang, Zhiyuan Li, Xinyu Wang, Hongyu Liu, Ruofan Liang, Songchun Zhang, Yuxuan Xue, Qifeng Chen
Abstract
We present LiveLight, the first diffusion-based framework for real-time streaming video relighting with interactive 3D lighting control. Achieving this is non-trivial, as it requires overcoming three critical challenges: effectively injecting dynamic 3D lighting into a diffusion model, maintaining high-fidelity generation under an extremely low NFE (Number of Function Evaluations) budget for real-time speed, and facilitating continuous streaming for interactive control. To address these pain points, we propose three key designs. First, for accurate lighting injection, we propose a lightweight adapter that feeds Multi-Plane Light Irradiance (MPLI) conditions-depth-aware irradiance maps encoding 3D lighting geometry-directly into the diffusion backbone. Second, to prevent rendering quality degradation at low NFEs towards real-time distillation, we introduce a geometry-guided feedback branch. This training-time constraint leverages a frozen geometry estimator to enforce depth- and normal-consistent relighting, ensuring geometrically plausible shading without adding inference overhead. Finally, to enable streaming interaction, we develop a progressive rolling-window strategy that maintains a denoising ladder of latent chunks at varying noise levels. By propagating intermediate states, this strategy guarantees temporal coherence and supports arbitrarily long video relighting with per-frame reference refresh. Extensive experiments on real-world and synthetic benchmarks demonstrate that LiveLight achieves state-of-the-art relighting quality while running at real-time speed, significantly outperforming offline baselines in temporal stability, lighting controllability, and user preference. To foster real-time interactive relighting research, we will publicly release our models, training data, and synthetic data generator.