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Video transition effects are widely used in video editing to connect shotsfor creating cohesive and visually appealing videos. However, it is challengingfor non-professionals to choose best transitions due to the lack ofcinematographic knowledge and design skills. In this paper, we present thepremier work on performing automatic video transitions recommendation (VTR):given a sequence of raw video shots and companion audio, recommend videotransitions for each pair of neighboring shots. To solve this task, we collecta large-scale video transition dataset using publicly available video templateson editing softwares. Then we formulate VTR as a multi-modal retrieval problemfrom vision/audio to video transitions and propose a novel multi-modal matchingframework which consists of two parts. First we learn the embedding of videotransitions through a video transition classification task. Then we propose amodel to learn the matching correspondence from vision/audio inputs to videotransitions. Specifically, the proposed model employs a multi-modal transformerto fuse vision and audio information, as well as capture the context cues insequential transition outputs. Through both quantitative and qualitativeexperiments, we clearly demonstrate the effectiveness of our method. Notably,in the comprehensive user study, our method receives comparable scores comparedwith professional editors while improving the video editing efficiency by\textbf{300\scalebox{1.25}{$\times$}}. We hope our work serves to inspire otherresearchers to work on this new task. The dataset and codes are public at\url{https://github.com/acherstyx/AutoTransition}.

AutoTransition: Learning to Recommend Video Transition Effects