Previous face inverse rendering methods often require synthetic data with ground truth and/or professional equipment like a lighting stage. However, a model trained on synthetic data or using pre-defined lighting priors is typically unable to generalize well for real-world situations, due to the gap between synthetic data/lighting priors and real data. Furthermore, for common users, the professional equipment and skill make the task expensive and complex. In this paper, we propose a deep learning framework to disentangle face images in the wild into their corresponding albedo, normal, and lighting components. Specifically, a decomposition network is built with a hierarchical subdivision strategy, which takes image pairs captured from arbitrary viewpoints as input. In this way, our approach can greatly mitigate the pressure from data preparation, and significantly broaden the applicability of face inverse rendering. Extensive experiments are conducted to demonstrate the efficacy of our design, and show its superior performance in face relighting over other state-of-the-art alternatives. Our code is available at https://github.com/AutoHDR/HD-Net.git .

中文翻译：

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通过分层解耦进行人脸逆向渲染

以前的人脸逆渲染方法通常需要具有地面实况和/或专业设备（如照明舞台）的合成数据。然而，由于合成数据/照明先验与真实数据之间的差距，在合成数据上训练或使用预定义照明先验的模型通常无法很好地概括现实世界的情况。此外，对于普通用户而言，专业的设备和技能使任务昂贵且复杂。在本文中，我们提出了一个深度学习框架，将野外的人脸图像分解为相应的反照率、法线和光照分量。具体来说，分解网络是使用分层细分策略构建的，该策略将从任意视点捕获的图像对作为输入。通过这种方式，我们的方法可以大大减轻数据准备的压力，并显着拓宽了人脸逆渲染的适用性。进行了广泛的实验以证明我们设计的有效性，并显示其在面部重新照明方面优于其他最先进的替代方案。我们的代码可在https://github.com/AutoHDR/HD-Net.git .