论文标题
指导的共同调节的GAN，用于360°视场外推
Guided Co-Modulated GAN for 360° Field of View Extrapolation
论文作者
论文摘要
我们提出了一种从单个图像中推断出360°视场的方法，该图像允许用户控制的综合外涂料。为此，我们提出改进基于GAN的模式架构，以进行底漆全景图表。我们的方法获得了最先进的结果，并且优于标准图像质量指标的先前方法。为了允许受控的外部修饰的合成，我们引入了一个新型的指导共调整框架，该框架通过常见的鉴别模型驱动图像生成过程。这样做可以保持生成的全景图的高视觉质量，同时在推断的视野中启用用户控制的语义内容。我们在定性和定量上均展示了我们方法的最新结果，从而提供了对我们新颖的编辑功能的彻底分析。最后，我们证明我们的方法受益于在照片中对高光泽对象的光真逼真的虚拟插入。
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 insertion of highly glossy objects in photographs.
