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We present ASSET, a neural architecture for automatically modifying an inputhigh-resolution image according to a user's edits on its semantic segmentationmap. Our architecture is based on a transformer with a novel attentionmechanism. Our key idea is to sparsify the transformer's attention matrix athigh resolutions, guided by dense attention extracted at lower imageresolutions. While previous attention mechanisms are computationally tooexpensive for handling high-resolution images or are overly constrained withinspecific image regions hampering long-range interactions, our novel attentionmechanism is both computationally efficient and effective. Our sparsifiedattention mechanism is able to capture long-range interactions and context,leading to synthesizing interesting phenomena in scenes, such as reflections oflandscapes onto water or flora consistent with the rest of the landscape, thatwere not possible to generate reliably with previous convnets and transformerapproaches. We present qualitative and quantitative results, along with userstudies, demonstrating the effectiveness of our method.

ASSET: Autoregressive Semantic Scene Editing with Transformers at High Resolutions