论文标题
多样性合理的360度图像支出用于有效的3DCG背景创建
Diverse Plausible 360-Degree Image Outpainting for Efficient 3DCG Background Creation
论文作者
论文摘要
我们通过估计其周围环境来解决从单个图像中生成360度图像的问题。以前的方法遭受了过度适应培训解决方案和确定性产生的损失。本文提出了一种使用变压器进行场景建模和新颖方法的完成方法，以提高输出图像上360度图像的属性。具体来说，我们使用带有变压器的完成网络来执行各种完成和调整网络，以匹配颜色，缝线和分辨率，并通过输入图像进行分辨率，从而在任何分辨率下都可以推断。为了改善在输出图像上360度图像的性能，我们还提出了感知损失和循环推断。彻底的实验表明，我们的方法在定性和定量上都优于最先进的方法（SOTA）方法。例如，与SOTA方法相比，我们的方法完成了分辨率大16倍的图像，并达到了较低的特征构成距离（FID）的1.7倍。此外，我们提出了一条管道，该管道使用完成结果和3DCG场景的背景。我们合理的背景完成可以在感知上自然产生带有镜面表面的虚拟对象的应用。
We address the problem of generating a 360-degree image from a single image with a narrow field of view by estimating its surroundings. Previous methods suffered from overfitting to the training resolution and deterministic generation. This paper proposes a completion method using a transformer for scene modeling and novel methods to improve the properties of a 360-degree image on the output image. Specifically, we use CompletionNets with a transformer to perform diverse completions and AdjustmentNet to match color, stitching, and resolution with an input image, enabling inference at any resolution. To improve the properties of a 360-degree image on an output image, we also propose WS-perceptual loss and circular inference. Thorough experiments show that our method outperforms state-of-the-art (SOTA) methods both qualitatively and quantitatively. For example, compared to SOTA methods, our method completes images 16 times larger in resolution and achieves 1.7 times lower Frechet inception distance (FID). Furthermore, we propose a pipeline that uses the completion results for lighting and background of 3DCG scenes. Our plausible background completion enables perceptually natural results in the application of inserting virtual objects with specular surfaces.
