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Comparing Effects of Different Cinematic Visualization Strategies on Viewer Comprehension

By Arnav Jhala and R. Michael Young


Abstract. Computational storytelling systems have mainly focused on the construction and evaluation of textual discourse for communicating stories. Few intelligent camera systems have been built in 3D environments for effective visual communication of stories. The evaluation of effectiveness of these systems, if any, has focused mainly on the run-time performance of the camera placement algorithms. The purpose of this paper is to present a systematic cognitive-based evaluation methodology to compare effects of different cinematic visualization strategies on viewer comprehension of stories. In particular, an evaluation of automatically generated visualizations from Darshak, a cinematic planning system, against different hand-generated visualization strategies is presented. The methodology used in the empirical evaluation is based on QUEST, a cognitive framework for question-answering in the context of stories, that provides validated predictors for measuring story coherence in readers. Data collected from viewers, who watch the same story renedered with three different visualization strategies, is compared with QUEST’s predictor metrics. Initial data analysis establishes significant effect on choice of visualization strategy on story comprehension. It further shows a significant effect of visualization strategy selected by Darshak on viewers ’ measured story coherence

Topics: Intelligent Camera Control, Computational Models of Narrative, Discourse Comprehension, Visual Discourse
Year: 2013
OAI identifier: oai:CiteSeerX.psu:
Provided by: CiteSeerX
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