We present a novel approach to single-view face relighting in the wild. Handling non-diffuse effects, such as global illumination or cast shadows, has long been a challenge in face relighting. Prior work often assumes Lambertian surfaces, simplified lighting models or involves estimating 3D shape, albedo, or a shadow map. This estimation, however, is error-prone and requires many training examples with lighting ground truth to generalize well. Our work bypasses the need for accurate estimation of intrinsic components and can be trained solely on 2D images without any light stage data, multi-view images, or lighting ground truth. Our key idea is to leverage a conditional diffusion implicit model (DDIM) for decoding a disentangled light encoding along with other encodings related to 3D shape and facial identity inferred from off-the-shelf estimators. We also propose a novel conditioning technique that eases the modeling of the complex interaction between light and geometry by using a rendered shading reference to spatially modulate the DDIM. We achieve state-of-the-art performance on standard benchmark Multi-PIE and can photorealistically relight in-the-wild images. Please visit our page: https://diffusion-face-relighting.github.io

翻译：我们提出了一种新颖的野外单视角人脸重光照方法。处理非漫反射效应（如全局光照或投射阴影）一直是人脸重光照领域的难点。现有方法通常假设朗伯表面、简化光照模型，或需要估计三维形状、反照率或阴影图。然而，此类估计易产生误差，且需大量包含光照真值的训练样本才能实现良好泛化。我们的方法绕过了对内在成分精确估计的需求，仅需二维图像即可完成训练，无需任何光场数据、多视角图像或光照真值。核心创新在于利用条件扩散隐式模型（DDIM）解耦光照编码，并联合从现成估计器推断的三维形状与人脸身份编码进行解码。我们同时提出一种新型条件技术：通过渲染的明暗参考图对DDIM进行空间调制，从而简化光照与几何复杂交互的建模过程。该方法在标准基准测试集Multi-PIE上取得了当前最优性能，并能以照片级真实感重光照野外图像。详情请访问：https://diffusion-face-relighting.github.io