Current work on image-based story generation suffers from the fact that the existing image sequence collections do not have coherent plots behind them. We improve visual story generation by producing a new image-grounded dataset, Visual Writing Prompts (VWP). VWP contains almost 2K selected sequences of movie shots, each including 5-10 images. The image sequences are aligned with a total of 12K stories which were collected via crowdsourcing given the image sequences and a set of grounded characters from the corresponding image sequence. Our new image sequence collection and filtering process has allowed us to obtain stories that are more coherent and have more narrativity compared to previous work. We also propose a character-based story generation model driven by coherence as a strong baseline. Evaluations show that our generated stories are more coherent, visually grounded, and have more narrativity than stories generated with the current state-of-the-art model.
翻译:目前关于以图像为基础的故事生成的工作由于以下事实而受到影响:现有的图像序列收藏没有前后连贯的图案。我们通过制作一个新的图像背景数据集,即视觉写字提示(VWP)来改进视觉故事生成。VWP包含近2K选取的电影拍摄序列,每个序列包括5-10个图像。图像序列与总共12K故事相匹配,这些故事是通过众包收集的,因为图像序列和一组来自相应图像序列的底部人物所收集的。我们新的图像序列收集和过滤过程使我们能够获得比以往工作更加连贯和更加有共鸣性的故事。我们还提出了一个以一致性驱动的基于字符的故事生成模型,作为强有力的基线。评估表明,我们所生成的故事比目前最先进的模型所生成的故事更加连贯、有视觉依据,而且具有更大的共鸣性。