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，能够对照片中的多种光照属性实现精确且连续的控制。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，如强度、颜色、环境光照、漫反射程度以及三维光源位置。模型在大规模带有真实光照标注的合成数据集上训练，辅以少量真实拍摄数据以增强真实感和泛化能力。我们在合成图像与真实图像上验证了该方法在多种重光照任务中的表现，包括控制场景内照明设备以及使用虚拟光源编辑环境光照。与先前工作相比，我们的方法在定量和定性指标上均达到了最优水平。值得注意的是，该方法无需显式的逆渲染监督，就能内在地理解光线与场景几何、遮挡及材质的交互作用，即使在传统挑战性场景（如将光源置于物体内部或对透明材质进行可信的重光照）中也能生成令人信服的光照效果。项目页面：vrroom.github.io/tokenlight/