We present SyncLight, the first method to enable consistent, parametric relighting across multiple uncalibrated views of a static scene. 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 sources and newly designed scenes -- alongside high-fidelity, real-world multi-view captures under calibrated illumination. Surprisingly, though trained only on image pairs, SyncLight generalizes zero-shot to an arbitrary number of viewpoints, effectively propagating lighting changes across all views, without requiring camera pose information. SyncLight enables practical relighting workflows for multi-view capture systems.

翻译：本文提出SyncLight，这是首个能够在静态场景的多个未标定视角间实现一致、参数化重光照的方法。尽管单视角重光照已取得显著进展，但现有的生成式方法难以维持多摄像机广播、立体电影和虚拟制作所必需的严格光照一致性。SyncLight通过基于单一参考编辑，实现对场景多视角采集图像中光照强度和颜色的精确控制，从而解决这一问题。我们的方法利用基于潜在桥匹配公式训练的多视角扩散Transformer，在单次推理步骤中实现整个图像集的高保真重光照。为促进训练，我们引入了一个大规模混合数据集，该数据集包含多样化的合成环境（从现有资源精选和新设计的场景）以及在标定光照下的高保真实世界多视角采集数据。值得注意的是，尽管仅使用图像对进行训练，SyncLight能够零样本泛化至任意数量的视角，有效地将光照变化传播到所有视图中，且无需相机姿态信息。SyncLight为多视角采集系统实现了实用的重光照工作流程。