GANs Spatial Control via Inference-Time Adaptive Normalization

Karin Jakoel, Liron Efraim, Tamar Rott Shaham; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022, pp. 2160-2169

We introduce a new approach for spatial control over the generation process of Generative Adversarial Networks (GANs). Our approach includes modifying the normalization scheme of a pre-trained GAN at test time, so as to act differently at different image regions, according to guidance from the user. This enables to achieve different generation effects at different locations across the image. In contrast to previous works that require either fine-tuning the model's parameters or training an additional network, our approach uses the pre-trained GAN as is, without any further modifications or training phase. Our method is thus completely generic and can be easily incorporated into common GAN models. We prove our technique to be useful for solving a line of image manipulation tasks, allowing different generation effects across the image, while preserving the GAN's high visual quality.