Sunjin Jung, Yeongho Seol, Kwanggyoon Seo, Hyeonho Na, Seonghyeon Kim, Vanessa Tan, Junyong Noh
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引用次数: 0
Abstract
We present a novel method that can generate realistic speech animations of a 3D face from audio using multiple adaptive windows. In contrast to previous studies that use a fixed size audio window, our method accepts an adaptive audio window as input, reflecting the audio speaking rate to use consistent phonemic information. Our system consists of three parts. First, the speaking rate is estimated from the input audio using a neural network trained in a self-supervised manner. Second, the appropriate window size that encloses the audio features is predicted adaptively based on the estimated speaking rate. Another key element lies in the use of multiple audio windows of different sizes as input to the animation generator: a small window to concentrate on detailed information and a large window to consider broad phonemic information near the center frame. Finally, the speech animation is generated from the multiple adaptive audio windows. Our method can generate realistic speech animations from in-the-wild audios at any speaking rate, i.e., fast raps, slow songs, as well as normal speech. We demonstrate via extensive quantitative and qualitative evaluations including a user study that our method outperforms state-of-the-art approaches.
期刊介绍:
ACM Transactions on Graphics (TOG) is a peer-reviewed scientific journal that aims to disseminate the latest findings of note in the field of computer graphics. It has been published since 1982 by the Association for Computing Machinery. Starting in 2003, all papers accepted for presentation at the annual SIGGRAPH conference are printed in a special summer issue of the journal.