基于计划的交互式故事叙述的基于草图的交互

E. S. D. Lima, Felipe João Gheno, A. Viseu
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引用次数: 3

摘要

几千年来,绘画一直被用作口头和书面故事的视觉补充。科技的发展和互动叙事的出现为探索绘画和讲故事的新方式带来了可能性。本文提出了一种新的基于草图的交互式故事系统交互方法,该方法使用基于卷积神经网络的深度学习模型来识别数字手绘草图。通过将实时素描识别与基于规划的情节生成算法相结合,该系统允许用户通过在智能手机或平板电脑上绘制物体素描来与叙事互动,然后系统识别这些物体并将其转换为故事世界中的虚拟物体,从而影响叙事的情节。初步结果表明,该模型对小组素描类具有显著的识别准确率(14个素描类的识别准确率为95.1%),足以提供丰富的交互选项。此外,它还可以扩展到更复杂的场景,同时保持相当高的准确率(172类87.4%,345类71.6%)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sketch-Based Interaction for Planning-Based Interactive Storytelling
Drawings have been used for thousands of years as a visual complement to oral and written storytelling. The evolution of technology and the advent of interactive narratives brings the possibility of exploring drawings and storytelling in new ways. This paper presents a new sketch-based interaction method for planning-based interactive storytelling systems, which uses a deep learning model based on a Convolutional Neural Network to recognize digital hand-drawn sketches. By combining real time sketch recognition with a planning-based plot generation algorithm, the proposed system allows users to interact with narratives by sketching objects on smartphones or tablet computers, which are then recognized by the system and converted into virtual objects in the story world, thereby affecting the plot of the narrative. Preliminary results show that the sketch recognition model has a remarkable accuracy for small sets of sketch classes (accuracy of 95.1 % for 14 classes), which are sufficient to provide a good variety of interaction options. In addition, it can also be extended to more complex scenarios while maintaining a considerable accuracy (87.4% for 172 classes and 71.6% for 345 classes).
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