情感引导语言驱动的面部动画

Sewhan Chun, Daegeun Choe, Shindong Kang, Shounan An, Youngbak Jo, Insoo Oh
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引用次数: 1

摘要

现代深度神经网络使得语音驱动的面部动画达到了适用的水平,从语音数据中模拟自然而精确的3D动画。尽管如此,许多作品在激烈的情感表达和动画的灵活性方面存在弱点。在这项工作中,我们引入了情感引导的语音驱动面部动画,同时对语音数据进行分类和回归,从而在面部动画中产生可控水平的明显情感表达。使用该方法的性能表现出合理的面部表情表现力和可控的灵活性。大量的实验表明,与以前的方法相比,我们的方法产生了更具表现力的面部动画,并且具有可控的灵活性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Emotion Guided Speech-Driven Facial Animation
The modern deep neural network has allowed an applicable level of speech-driven facial animation, simulating natural and precise 3D animation from speech data. Regardless, many of the works show weakness in drastic emotional expression and flexibility of the animation. In this work, we introduce emotion guided speech-driven facial animation, simultaneously proceeding with classification and regression from the speech data to generate a controllable level of evident emotional expression on facial animation. Performance using our method shows reasonable expressiveness of facial emotion with controllable flexibility. Extensive experiments indicate that our method generates more expressive facial animation with controllable flexibility compared to previous approaches.
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