It is in high demand to generate facial animation with high realism, but it remains a challenging task. Existing approaches of speech-driven facial animation can produce satisfactory mouth movement and lip synchronization, but show weakness in dramatic emotional expressions and flexibility in emotion control. This paper presents a novel deep learning-based approach for expressive facial animation generation from speech that can exhibit wide-spectrum facial expressions with controllable emotion type and intensity. We propose an emotion controller module to learn the relationship between the emotion variations (e.g., types and intensity) and the corresponding facial expression parameters. It enables emotion-controllable facial animation, where the target expression can be continuously adjusted as desired. The qualitative and quantitative evaluations show that the animation generated by our method is rich in facial emotional expressiveness while retaining accurate lip movement, outperforming other state-of-the-art methods.
翻译:以高度现实主义生成面部动画的要求很高,但这仍是一项艰巨的任务。 由语言驱动的面部动画的现有方法可以产生满意的嘴部运动和嘴唇同步,但是在情绪控制方面表现出巨大的情感表达力和灵活性的弱点。 本文展示了一种全新的深层次的学习方法,从表达面部动画生成出一种能够展现可控情绪类型和强度的广度面部表达式的言语。 我们提议了一个情感控制模块,以了解情感变化(如类型和强度)与相应的面部表达参数之间的关系。 它可以使可控制情感的面部动画能够按需要持续调整。 定性和定量评估显示,我们方法产生的动画在保持准确的嘴唇运动的同时,也优于其他最先进的方法。