# Generation of Stereo Images Based on a View Synthesis Network
## 1 Introduction  
In recent years, because of the commercialization of wearable systems and the vigorous development of related technologies, research on virtual reality has become increasingly popular. Among many related topics, the concept of stereo images is basic and essential. In the real world, the human eye recognizes three-dimensional objects, which is termed a stereoscopic sense [1]. Many studies have been conducted on stereo images, on such issues as the design of special cameras or devices to capture stereoscopic panoramas [2] and stitching stereoscopic panoramas from stereo images captured with a stereo camera [3]. Research on stereo images also benefits from the recent dramatic development of deep learning. After several representative networks were proposed, such as convolutional neural networks (CNNs) [4; 5] and generative adversarial networks (GANs) [6], correlated studies on stereo images witnessed breakthrough developments, such as stereo matching and disparity estimation from a pair of stereo images [7; 8], single view depth estimation [9; 10; 11; 12; 13], predicting new views or constructing a complete three-dimensional scene from an image sequence [14; 15], and view synthesis for an object from only a single view [16; 17; 18; 19; 20]. However, these studies lack a method for generating a pair of stereo images from a single image. For example, if there is only one photo in an album, it cannot be used to obtain stereo images to present stereoscopic vision using the aforementioned methods. [PERSON] et al. [21] proposed a CNN-based method to automatically convert videos from two to three dimensions, where a single image is input as the left-eye view, and the disparity of each pixel is predicted using the network to generate the right-eye view. The input (left-eye view) and output (right-eye view) can be combined into a red-cyan anaglyph. A drawback of this method is that it is based on the conventional warping method, and considers only the disparity of each pixel and not the rotation of the entire object. The conventional warping method warps an image using the disparity between each pixel in the left- and right-eye views, based on the different depths of pixels in the image. It only considers translations of pixels. However, when both eyes gaze at the same object in a scene, the object between the different views of the eyes appears to not only be translated, but also rotated. The rotation is an essential factor that affects the stereoscopic sense. Although the traditional warping method may display similar effects, because of different translation values of each pixel in the same object, the resulting image is usually too distorted or blurred and the rotation is insignificant, resulting in insufficient stereoscopic sense. Furthermore, in the conventional warping method, the pixels that are not in the original image but can be observed in the resulting image (i.e., the part that cannot be seen in the original image because of the field of view) cannot be effectively processed. Therefore, in this paper, we propose a new model that generates stereo images from a single image, considering both translations as well as the rotation of objects in the scene. Methods that consider both translation and rotation can be found in many studies of view synthesis [14; 20; 22]. [PERSON] et al. [14] proposed a method that is similar to complementing frames in a video. They used an image sequence (e.g., a video) captured in motion as an input, and employed a network to predict a new view between two frames. Although their method can obtain high-quality results, it cannot be applied to a single image. Methods proposed by [PERSON] et al. [17] and [PERSON] et al. [20] have produced results on a single object, and their networks could effectively predict the portion of the object that was occluded in the original view. However, when these methods are applied to scene prediction, even if the approximate structure and texture can be generated, a considerable amount of distortion and blur may occur.