[2204.07286] Guided Co-Modulated GAN for 360° Field of View Extrapolation

We propose a method to extrapolate a 360° field of view from a single image that allows for user-controlled synthesis of the out-painted content. To do so, we propose improvements to an existing GAN-based in-painting architecture for out-painting panoramic image representation. Our method obtains state-of-the-art results and outperforms previous methods on standard image quality metrics. To allow controlled synthesis of out-painting, we introduce a novel guided co-modulation framework, which drives the image generation process with a common pretrained discriminative model. Doing so maintains the high visual quality of generated panoramas while enabling user-controlled semantic content in the extrapolated field of view. We demonstrate the state-of-the-art results of our method on field of view extrapolation both qualitatively and quantitatively, providing thorough analysis of our novel editing capabilities. Finally, we demonstrate that our approach benefits the photorealistic virtual in

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@allowfirm
@allowfirm 慶應のCVPR 2022採択の研究( t.co/IrLyARO0eo )も360度の画像生成で、今月発表のarXiv論文( t.co/P4J5HRz8lR )と手法の特色を比較してみたら面白そうと思いました。

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