Aligning Latent and Image Spaces To Connect the Unconnectable

Ivan Skorokhodov, Grigorii Sotnikov, Mohamed Elhoseiny; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 14144-14153

In this work, we develop a method to generate infinite high-resolution images with diverse and complex content. It is based on a perfectly equivariant patch-wise generator with synchronous interpolations in the image and latent spaces. Latent codes, when sampled, are positioned on the coordinate grid, and each pixel is computed from an interpolation of the neighboring codes. We modify the AdaIN mechanism to work in such a setup and train a GAN model to generate images positioned between any two latent vectors. At test time, this allows for generating infinitely large images of diverse scenes that transition naturally from one into another. Apart from that, we introduce LHQ: a new dataset of 90k high-resolution nature landscapes. We test the approach on LHQ, LSUN Tower and LSUN Bridge and outperform the baselines by at least 4 times in terms of quality and diversity of the produced infinite images. The project website is located at https://universome.github.io/alis.