Cinemagraphs are a compelling way to convey dynamic aspects of a scene. In these media, dynamic and still elements are juxtaposed to create an artistic and narrative experience. Creating a high-quality, aesthetically pleasing cinemagraph requires isolating objects in a semantically meaningful way and then selecting good start times and looping periods for those objects to minimize visual artifacts (such a tearing). To achieve this, we present a new technique that uses object recognition and semantic segmentation as part of an optimization method to automatically create cinemagraphs from videos that are both visually appealing and semantically meaningful. Given a scene with multiple objects, there are many cinemagraphs one could create. Our method evaluates these multiple candidates and presents the best one, as determined by a model trained to predict human preferences in a collaborative way. We demonstrate the effectiveness of our approach with multiple results and a user study.



We would like to thank all the participants in our user study. We are also grateful to Jian Sun and Jinwoo Shin for the helpful discussions. This work was mostly done while the first author was an intern at Microsoft Research, Redmond. It was completed at KAIST with the support of the Technology Innovation Program (No. 10048320), which is funded by the Korean government (MOTIE).

** The first and second authors contributed equally to this work.