LIFI: Towards Linguistically Informed Frame Interpolation

• URL: http://arxiv.org/abs/2010.16078v5
• Date: Wed, 2 Dec 2020 16:47:06 GMT
• Title: LIFI: Towards Linguistically Informed Frame Interpolation
• Authors: Aradhya Neeraj Mathur, Devansh Batra, Yaman Kumar, Rajiv Ratn Shah, Roger Zimmermann
• Abstract summary: We try to solve this problem by using several deep learning video generation algorithms to generate missing frames. We release several datasets to test computer vision video generation models of their speech understanding.
• Score: 66.05105400951567
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In this work, we explore a new problem of frame interpolation for speech videos. Such content today forms the major form of online communication. We try to solve this problem by using several deep learning video generation algorithms to generate the missing frames. We also provide examples where computer vision models despite showing high performance on conventional non-linguistic metrics fail to accurately produce faithful interpolation of speech. With this motivation, we provide a new set of linguistically-informed metrics specifically targeted to the problem of speech videos interpolation. We also release several datasets to test computer vision video generation models of their speech understanding.

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