This paper presents a method for image relighting that enables precise and continuous control over multiple illumination attributes in a photograph. We formulate relighting as a conditional image generation task and introduce attribute tokens to encode distinct lighting factors such as intensity, color, ambient illumination, diffuse level, and 3D light positions. The model is trained on a large-scale synthetic dataset with ground-truth lighting annotations, supplemented by a small set of real captures to enhance realism and generalization. We validate our approach across a variety of relighting tasks, including controlling in-scene lighting fixtures and editing environment illumination using virtual light sources, on synthetic and real images. Our method achieves state-of-the-art quantitative and qualitative performance compared to prior work. Remarkably, without explicit inverse rendering supervision, the model exhibits an inherent understanding of how light interacts with scene geometry, occlusion, and materials, yielding convincing lighting effects even in traditionally challenging scenarios such as placing lights within objects or relighting transparent materials plausibly. Project page: vrroom.github.io/tokenlight/

翻译：本文提出了一种图像重光照方法，可在照片中实现对多种光照属性的精确且连续的控制。我们将重光照建模为条件图像生成任务，并引入属性令牌来编码不同的光照因子，例如强度、颜色、环境光照、漫射程度以及三维光源位置。该模型在大规模合成数据集上训练，并辅以少量真实拍摄数据以增强真实感与泛化能力。我们在多种重光照任务上验证了该方法，包括控制场景内光照设备和使用虚拟光源编辑环境光照，涵盖合成与真实图像。与先前工作相比，本方法在定量和定性指标上均达到了最先进水平。值得注意的是，在没有显式逆渲染监督的情况下，模型展现了对光与场景几何、遮挡及材质交互作用的内在理解，能在诸如将光源置于物体内部或对透明材质进行可信重光照等传统挑战性场景中，生成令人信服的光照效果。项目页面：vrroom.github.io/tokenlight/