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We address the problem of generating a 360-degree image from a single imagewith a narrow field of view by estimating its surroundings. Previous methodssuffered from overfitting to the training resolution and deterministicgeneration. This paper proposes a completion method using a transformer forscene modeling and novel methods to improve the properties of a 360-degreeimage on the output image. Specifically, we use CompletionNets with atransformer to perform diverse completions and AdjustmentNet to match color,stitching, and resolution with an input image, enabling inference at anyresolution. To improve the properties of a 360-degree image on an output image,we also propose WS-perceptual loss and circular inference. Thorough experimentsshow that our method outperforms state-of-the-art (SOTA) methods bothqualitatively and quantitatively. For example, compared to SOTA methods, ourmethod completes images 16 times larger in resolution and achieves 1.7 timeslower Frechet inception distance (FID). Furthermore, we propose a pipeline thatuses the completion results for lighting and background of 3DCG scenes. Ourplausible background completion enables perceptually natural results in theapplication of inserting virtual objects with specular surfaces.

Diverse Plausible 360-Degree Image Outpainting for Efficient 3DCG Background Creation