In this work, we propose to solve ill-posed inverse imaging problems using a bank of Generative Adversarial Networks (GAN) as a prior and apply our method to the case of Intrinsic Image Decomposition for faces and materials. Our method builds on the demonstrated success of GANs to capture complex image distributions. At the core of our approach is the idea that the latent space of a GAN is a well-suited optimization domain to solve inverse problems. Given an input image, we propose to jointly inverse the latent codes of a set of GANs and combine their outputs to reproduce the input. Contrary to most GAN inversion methods which are limited to inverting only a single GAN, we demonstrate that it is possible to maintain distribution priors while inverting several GANs jointly. We show that our approach is modular, allowing various forward imaging models, that it can successfully decompose both synthetic and real images, and provides additional advantages such as leveraging properties of GAN latent space for image relighting.

该研究提出使用GAN作为先验来解决不适定的反演成像问题，并将方法应用于面部和材料的固有图像分解。该方法建立在GAN成功捕捉复杂图像分布的基础上，并展示了通过逆转多个GAN来维持分布先前的可能性，其是模块化的，可以成功地分解合成和真实图像，并利用GAN潜在空间的属性，提供了额外的优势，如图像重照。

JoIN:联合GAN逆向用于固有图像分解