发表机构
Stanford University; Georgia Institute of Technology; University of Nevada, Reno; University of Buffalo（斯坦福大学; 佐治亚理工学院; 内华达大学里诺分校; 布法罗大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Tellimation通过即时动画可视化儿童叙事中的空白，覆盖八种空白类型，利用实时检测与参数化动画，显著提升儿童对叙事空白的解决率。
AI 中文摘要
插图场景常被用来激发儿童讲故事，但有语言困难的儿童往往会遗漏使故事连贯的细节、动作和关系。我们引入了Tellimation，该系统将儿童遗漏或错误描述的场景元素进行动画化，在不告知该说什么的情况下吸引对叙事空白的注意。其设计空间涵盖八种空白（场景中有谁、有多少、他们是什么样、他们在做什么、在哪里、何时、他们之间的关系，以及画面之外的内容，如言语和想法），并通过20个基于经典动画原理的参数化动画实例化。一个实时流程检测话语与场景之间的不一致，然后根据场景的叙事潜力和儿童的历史选择并参数化一个动画。成年人在无需指导的情况下理解了大多数动画（N=120）；儿童（N=12）在系统通过动画识别的空白中解决了46%，而没有动画时仅为13%。
英文摘要
Illustrated scenes are used to prompt children's stories, yet children with language difficulties often omit the details, actions, and relationships that make a story coherent. We introduce Tellimation, which animates scene elements a child has omitted or misdescribed, drawing attention to a narrative gap without saying what to tell. Its design space covers eight kinds of gaps (who is in the scene, how many, what they are like, what they are doing, where, when, how they relate, and what lies beyond the picture, such as speech and thoughts), instantiated through 20 parameterized animations grounded in classical animation principles. A real-time pipeline detects discrepancies between utterance and scene, then selects and parameterizes an animation from the scene's narrative potential and the child's history. Adults interpreted most animations without instruction (N=120); children (N=12) resolved 46% of the gaps the system identified with animations, against 13% without.
