SyncLight: Single-Edit Multi-View Relighting
David Serrano-Lozano, Anand Bhattad, Luis Herranz, Jean-Fran\c{c}ois Lalonde, Javier Vazquez-Corral

TL;DR
SyncLight is a novel multi-view relighting method that ensures consistent, high-fidelity lighting control across uncalibrated views using a diffusion transformer trained on synthetic and real data.
Contribution
It introduces a multi-view diffusion transformer trained with a latent bridge matching formulation for zero-shot, multi-view relighting without camera calibration.
Findings
Achieves high-fidelity relighting in a single inference step.
Generalizes zero-shot to any number of viewpoints.
Trained on a large hybrid dataset of synthetic and real multi-view scenes.
Abstract
We present SyncLight, a method to enable consistent, parametric control over light sources across multiple uncalibrated views of a static scene conditioned on a single view. While single-view relighting has advanced significantly, existing generative approaches struggle to maintain the rigorous lighting consistency essential for multi-camera broadcasts, stereoscopic cinema, and virtual production. SyncLight addresses this by enabling precise control over light intensity and color across a multi-view capture of a scene, conditioned on a single reference edit. Our method leverages a multi-view diffusion transformer trained using a latent bridge matching formulation, achieving high-fidelity relighting of the entire image set in a single inference step. To facilitate training, we introduce a large-scale hybrid dataset comprising diverse synthetic environments -- curated from existing…
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