Learning to produce 3D media from a captured 2D video

Due to the advances in display technologies and the commercial success of 3D motion pictures in recent years, there is renewed interest in enabling consumers to create 3D content. While new 3D content can be created using more advanced capture devices (i.e., stereo cameras), most people still use 2D capture devices. Furthermore, enormously large collections of captured media exist only in 2D. We present a system for producing stereo images from captured 2D videos. Our system detects "good" stereo frames from a 2D video, which was captured a priori without any constraints on camera motion or content. We use a trained classifier to detect pairs of video frames that are suitable for constructing stereo images. In particular, for a given frame It at time t, we determine if t̂ exists such that It+t̂ and It can form an acceptable stereo image. We verify the performance of our method for producing stereo media from captured 2D videos in a psychovisual evaluation using both professional movie clips and amateur home videos. To the best of our knowledge, detecting good stereo pairs from a captured 2D video has been adequately addressed in the literature.