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 sources and newly designed scenes -- alongside high-fidelity, real-world multi-view captures under calibrated illumination. 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：我们提出SyncLight，一种能够在静态场景的多个未校准视角下实现一致且参数化光源控制的方法，其条件仅基于单个视图。尽管单视角重光照技术已取得显著进展，现有生成方法难以维持多摄像头广播、立体影像和虚拟制作所需的多视角光照严格一致性。SyncLight通过以下方式解决该问题：基于单次参考编辑，实现对场景多视角捕获数据中光源强度与色彩的精确控制。本方法采用基于隐变量桥接匹配公式训练的多视角扩散Transformer，在单次推理步骤中完成整个图像集合的高保真重光照。为促进训练，我们构建了一个大规模混合数据集，包含多样化合成环境（来自现有资源与新设计场景）以及经过标定光照的高保真真实多视角捕获数据。尽管仅使用图像对进行训练，SyncLight可零样本泛化至任意数量的视角，无需相机位姿信息即可将光照变化有效传播至所有视图。这为多视角捕获系统提供了实用的重光照工作流。