用属性令牌实现图像光照的精准连续控制
TokenLight: Precise Lighting Control in Images using Attribute Tokens
- 引入属性令牌编码光照强度、颜色等多维度因素
- 在合成与真实图像上均达到领先效果，无需逆渲染监督
- 适合需要精细光影编辑的影视、设计领域用户
本文提出一种图像重光照方法，可对照片中的多个照明属性实现精确且连续的控制。将重光照建模为条件图像生成任务，引入属性令牌来编码光照强度、颜色、环境光、漫反射程度及3D光位置等不同因素。模型在大规模带真值光照标注的合成数据集上训练，并辅以少量真实图像提升真实感与泛化能力。在合成与真实图像上验证了该方法在控制场景光源和使用虚拟光源编辑环境光方面的有效性。相比先前方法，本方法在定量与定性指标上均取得最优表现。尤为关键的是，在无显式逆渲染监督的情况下，模型展现出对光线与场景几何、遮挡及材质交互的内在理解，可在物体内部布光或重光照透明材质等传统难题中生成可信效果。
原文摘要 · Abstract (English)
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/
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