Title: LightCtrl: Training-free Controllable Video Relighting
Primary area: generative models
Keywords: video relighting; controllable video editing

Abstract: Recent diffusion models have achieved remarkable success in image relighting, and this success has quickly been reproduced in video relighting. Although these methods can relight videos under various conditions, their ability to explicitly control the illumination in the relighted video remains limited. Therefore, we present LightCtrl, the first controllable video relighting method that offers explicit control over the video illumination through a user-supplied light trajectory in a training-free manner. This is essentially achieved by leveraging a hybrid approach that combines pre-trained diffusion models: a pre-trained image relighting diffusion model is used to relight each frame individually, followed by a video diffusion prior that enhances the temporal consistency of the relighted sequence. In particular, to enable explicit control over dynamically varying lighting in the relighted video, we introduce two key components. 
First, the Light Map Injection module samples light trajectory-specific noise and injects it into the latent representation of the source video, significantly enhancing illumination coherence with respect to the conditional light trajectory. 
Second, the Geometry-Aware Relighting module dynamically combines RGB and normal map latents in the frequency domain to suppress the influence of the original lighting in the input video, thereby further improving the relighted video's adherence to the input light trajectory. 
Our experiments demonstrate that LightCtrl can generate high-quality video results with diverse illumination changes closely following the light trajectory condition, indicating improved controllability over baseline methods. 
The code will be released to facilitate future studies.

TL;DR: LightCtrl relight a given video via a user-specific light trajectory in a zero-shot manner.