  We present a novel framework, InfinityGAN, for arbitrary-sized image
generation. The task is associated with several key challenges. First, scaling
existing models to an arbitrarily large image size is resource-constrained, in
terms of both computation and availability of large-field-of-view training
data. InfinityGAN trains and infers in a seamless patch-by-patch manner with
low computational resources. Second, large images should be locally and
globally consistent, avoid repetitive patterns, and look realistic. To address
these, InfinityGAN disentangles global appearances, local structures, and
textures. With this formulation, we can generate images with spatial size and
level of details not attainable before. Experimental evaluation validates that
InfinityGAN generates images with superior realism compared to baselines and
features parallelizable inference. Finally, we show several applications
unlocked by our approach, such as spatial style fusion, multi-modal
outpainting, and image inbetweening. All applications can be operated with
arbitrary input and output sizes. Please find the full version of the paper at
https://openreview.net/forum?id=ufGMqIM0a4b .
