Yeyao Zhang
2019
Generating Animations from Screenplays
Yeyao Zhang
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Eleftheria Tsipidi
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Sasha Schriber
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Mubbasir Kapadia
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Markus Gross
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Ashutosh Modi
Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)
Automatically generating animation from natural language text finds application in a number of areas e.g. movie script writing, instructional videos, and public safety. However, translating natural language text into animation is a challenging task. Existing text-to-animation systems can handle only very simple sentences, which limits their applications. In this paper, we develop a text-to-animation system which is capable of handling complex sentences. We achieve this by introducing a text simplification step into the process. Building on an existing animation generation system for screenwriting, we create a robust NLP pipeline to extract information from screenplays and map them to the system’s knowledge base. We develop a set of linguistic transformation rules that simplify complex sentences. Information extracted from the simplified sentences is used to generate a rough storyboard and video depicting the text. Our sentence simplification module outperforms existing systems in terms of BLEU and SARI metrics. We further evaluated our system via a user study: 68% participants believe that our system generates reasonable animation from input screenplays.
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