We consider an emerging situation where multiple cameras are filming the same event simultaneously from a diverse set of angles. The captured videos provide us with the multiple view geometry and an understanding of the 3D structure of the scene. We further extend this understanding by introducing the concept of 3D interest map in this paper. As most users naturally film what they find interesting from their respective viewpoints, the 3D structure can be annotated with the level of interest, naturally crowdsourced from the users. A 3D interest map can be understood as an extension of saliency maps in the 3D space that captures the semantics of the scene. We evaluate the idea of 3D interest maps on two real datasets, taken from the environment or the cameras that are equipped enough to have an estimation of the poses of cameras and a reasonable synchronization between them. We study two aspects of the 3D interest maps in our evaluation. First, by projecting them into 2D, we compare them to state-of-the-art saliency maps. Second, to demonstrate the usefulness of the 3D interest maps, we apply them to a video mashup system that automatically produces an edited video from one of the datasets.

In this demonstration, we show an automated mobile video mashup system that takes a set of videos filming the same scene as input, and generate an output mashup video consisting of temporally coherent clips selected from these input videos. The key difference of our system over the existing state-of-the-art is that it can generate virtual close-up shots and three camera operations: zooming in, zooming out, and panning, automatically. To achieve this, the system first computes the motion maps of the input videos and then determines a set of rectangles that correspond to highly interesting regions (in terms of motion). The choice of which shot types to use is done heuristically, ensuring diversity and coherency in the content presented in the mashup.

In this technical demonstration, we demonstrate a Web-based application called Jiku Director that automatically creates a mashup video from event videos uploaded by users. The system runs an algorithm that considers view quality (shakiness, tilt, occlusion), video quality (blockiness, contrast, sharpness, illumination, burned pixels), and spatial-temporal diversity (shot angles, shot lengths) to create a mashup video with smooth shot transitions while covering the event from different perspectives.