We present PIXLRelight, a feed-forward approach for physically controllable single-image relighting. Existing methods either provide limited lighting control (e.g. through text or environment maps), accumulate errors when chaining inverse and forward rendering, or require costly per-image optimization. Our key idea is to bridge physically based rendering (PBR) and learned image synthesis through a shared intrinsic conditioning that can be obtained from either real photographs or PBR renders. At training time, paired multi-illumination photographs are decomposed into albedo, diffuse shading, and non-diffuse residuals, which condition the model. At inference time, the same conditioning is computed from a path-traced render of a coarse 3D reconstruction of the input under user-specified PBR lights. A transformer-based neural renderer then applies the target illumination to the source photograph, preserving fine image detail through a per-pixel affine modulation. PIXLRelight enables arbitrary PBR-style lighting control, achieves state-of-the-art relighting quality, and runs in under a tenth of a second per image. Code and models are available at https://mlfarinha.github.io/pixl-relight/.

翻译：我们提出PIXLRelight，一种用于单张图像物理可控重光照的前馈方法。现有方法要么提供有限的光照控制（例如通过文本或环境贴图），要么在链式执行正向与逆向渲染时累积误差，要么需要代价高昂的逐图像优化。核心思路在于：通过共享的内在条件约束（可从真实照片或基于物理渲染的渲染结果中获取），将基于物理的渲染与学习型图像合成相衔接。训练阶段，配对的多光照照片被分解为反照率、漫反射着色项和非漫反射残差，以此约束模型；推理阶段，对输入图像的粗粒度三维重建结果施加用户指定的PBR光源，通过路径追踪渲染计算相同的条件约束。随后，基于Transformer的神经渲染器通过逐像素仿射调制保留精细图像细节，将目标光照应用于原始照片。PIXLRelight支持任意PBR风格的光照控制，实现了当前最优的重光照质量，且每张图像的处理时间不足0.1秒。代码与模型开源地址：https://mlfarinha.github.io/pixl-relight/。