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Natural head motion synthesis driven by acoustic prosodic features

机译：声学韵律特征驱动的自然头部运动合成

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摘要

Natural head motion is important to realistic facial animation and engaging human-computer interactions. In this paper, we present a novel data-driven approach to synthesize appropriate head motion by sampling from trained hidden markov models (HMMs). First, while an actress recited a corpus specifically designed to elicit various emotions, her 3D head motion was captured and further processed to construct a head motion database that included synchronized speech information. Then, an HMM for each discrete head motion representation (derived directly from data using vector quantization) was created by using acoustic prosodic features derived from speech. Finally, first-order Markov models and interpolation techniques were used to smooth the synthesized sequence. Our comparison experiments and novel synthesis results show that synthesized head motions follow the temporal dynamic behavior of real human subjects.
机译：自然的头部运动对于逼真的面部动画和进行人机交互至关重要。在本文中，我们提出了一种新颖的数据驱动方法，通过从训练有素的隐马尔可夫模型（HMM）中进行采样来合成适当的头部运动。首先，当一位女演员朗诵专门设计用于引发各种情感的语料时，她的3D头部动作被捕获并进一步处理以构建包括同步语音信息的头部动作数据库。然后，通过使用从语音派生的声音韵律特征，为每个离散的头部运动表示（直接从使用矢量量化的数据得出）创建HMM。最后，使用一阶马尔可夫模型和插值技术对合成序列进行平滑处理。我们的比较实验和新颖的合成结果表明，合成的头部动作遵循真实人类对象的时间动态行为。

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