Towards automatic Stereoscopic Video Synthesis from a Casual Monocular video

Abstract—Automatically synthesizing 3D content from a causal monocular video has become an important problem. Previous works either use no geometry information, or rely on precise 3D geometry information. Therefore, they cannot obtain reasonable results if the 3D structure in the scene is complex, or noisy 3D geometry information is estimated from monocular videos. In this paper, we present an automatic and robust framework to synthesize stereoscopic videos from casual 2D monocular videos. First, 3D geometry information (e.g., camera parameters, depth map) are extracted from the 2D input video. Then a Bayesian-based View Synthesis (BVS) approach is proposed to render high-quality new virtual views for stereoscopic video to deal with noisy 3D geometry information. Extensive experiments on various videos demonstrate that BVS can synthesize more accurate views than other methods, and our proposed framework also be able to generate high-quality 3D videos. Keywords-Stereoscopic video synthesis, automatic, Bayesianbased View Synthesis. parameters, which cannot meet the requirements of current image-based rendering (IBR) methods [1], [2], [3]. Third, image quality of synthesized virtual views degrades greatly due to occlusions. While several methods have been proposed to address one or two of these challenges, to the best of our knowledge, none of them can deal with all of these challenges simultaneously. Fig. 1(a) and Fig. 1(b) show some of the poor results by [1], [2] due to these challenges. I