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A video storyboard is a roadmap for video creation which consists ofshot-by-shot images to visualize key plots in a text synopsis. Creating videostoryboards, however, remains challenging which not only requires cross-modalassociation between high-level texts and images but also demands long-termreasoning to make transitions smooth across shots. In this paper, we propose anew task called Text synopsis to Video Storyboard (TeViS) which aims toretrieve an ordered sequence of images as the video storyboard to visualize thetext synopsis. We construct a MovieNet-TeViS dataset based on the publicMovieNet dataset. It contains 10K text synopses each paired with keyframesmanually selected from corresponding movies by considering both relevance andcinematic coherence. To benchmark the task, we present strong CLIP-basedbaselines and a novel VQ-Trans. VQ-Trans first encodes text synopsis and imagesinto a joint embedding space and uses vector quantization (VQ) to improve thevisual representation. Then, it auto-regressively generates a sequence ofvisual features for retrieval and ordering. Experimental results demonstratethat VQ-Trans significantly outperforms prior methods and the CLIP-basedbaselines. Nevertheless, there is still a large gap compared to humanperformance suggesting room for promising future work. The code and data areavailable at: \url{https://ruc-aimind.github.io/projects/TeViS/}

TeViS:Translating Text Synopses to Video Storyboards