InfinityGAN: Towards Infinite-Resolution Image Synthesis

We present InfinityGAN, a method to generate arbitrary-resolution images. The problem is associated with several key challenges. First, scaling existing models to a high resolution is resource-constrained, both in terms of computation and availability of high-resolution training data. Infinity-GAN trains and infers patch-by-patch seamlessly with low computational resources. Second, large images should be locally and globally consistent, avoid repetitive patterns, and look realistic. To address these, InfinityGAN takes global appearance, local structure and texture into account.With this formulation, we can generate images with resolution and level of detail not attainable before. Experimental evaluation supports that InfinityGAN generates imageswith superior global structure compared to baselines at the same time featuring parallelizable inference. Finally, we how several applications unlocked by our approach, such as fusing styles spatially, multi-modal outpainting and image inbetweening at arbitrary input and output resolutions

https://arxiv.org/abs/2104.03963

任意分辨率图像生成任务有以下几个挑战:(1)高分辨率的图像生成要求高的资源消耗;(2)高分辨的图像各个部分应该保持一致,尽量避免重复的特征,并且要看起来真实。为了解决上述问题,本文提出InfinityGAN,一种可以生成任意分辨率图像的方法。我们的方法同时考虑全局外观、局部解构和纹理。因此我们可以生成之前方法无法生成的高分辨图像。

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