通过层次的隐含周期性学习实现统一的共同语音手势生成
概括
本研究介绍了一种层次隐含周期性 (HIP) 方法,用于从语音中实现现实的3D手势生成. 通过模拟头部,身体和手部运动之间的相关性,HIP提高了协调和自然性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 从语音生成现实的3D身体运动对于虚拟现实和动画等应用至关重要.
- 当前的方法往往因为模拟不同身体部位 (头部,身体,手) 的相互和内相互关联的局限性而与自然性和协调性作斗争.
研究的目的:
- 提出一种新的等级隐含周期性 (HIP) 学习方法,用于音频启发的3D手势生成.
- 通过明确建模内在相关性来解决共同语音手势生成的不良性质.
主要方法:
- 使用周期性自动编码器来探索手势运动阶段的多元体,捕获周期性和非周期性人类运动特征.
- 实施级联指导以建模面部表情,身体手势和手动之间的等级关系.
主要成果:
- 拟议的HIP方法在定量和定性评估中表现优于最先进的方法.
- 生成的3D手势在3D化身上表现出更好的自然性和协调性.
结论:
- 高语音学习方法有效地模拟了多模式隐性关系,以实现现实的共同语音手势生成.
- 这种方法在创造与语音同步的自然和协调的3D人类运动方面取得了重大进展.
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