Predicting the visual context of an image beyond its boundary.
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The challenging task of image outpainting (extrapolation) has received comparatively little attention in relation to its cousin, image inpainting (completion).
The second challenge is how to maintain high quality in generated results, especially for multi-step generations in which generated regions are spatially far away from the initial input.
The experimental results on this dataset have demonstrated the efficacy of our proposed network.
In this paper, we study the problem of generating a set ofrealistic and diverse backgrounds when given only a smallforeground region.
Although humans perform well at predicting what exists beyond the boundaries of an image, deep models struggle to understand context and extrapolation through retained information.
#2 best model for Image Outpainting on Places365-Standard
This way, the hallucinated details are integrated with the style of the original image, in an attempt to further boost the quality of the result and possibly allow for arbitrary output resolutions to be supported.