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语音驱动的个性化手势合成:利用自动模糊特征推理

Fan Zhang, Zhaohan Wang, Xin Lyu

IEEE transactions on visualization and computer graphics

|April 24, 2024

概括

Persona-Gestor使用新的模糊特征提取器和扩散模型从原始语音中生成个性化的3D手势. 这种方法增强了虚拟人类创造中的表现力和同步性.

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:

语音驱动的手势生成对于现实的虚拟人类至关重要.
现有的方法在复杂的多式联络输入和有限的表达力方面扎.
从语音中准确地提取特征仍然是一个重大挑战.

研究的目的:

介绍Persona-Gestor,这是一个端到端的生成模型,用于个性化的3D手势合成.
克服传统方法的局限性,仅依靠原始语音音频.
为了增强生成的手势的自然性,表现力和同步性.

主要方法:

开发了一种新的模糊特征提取器,使用模糊推理来捕获隐性语音特征.
采用非自行回归自适应层规范化 (AdaLN) 变压器扩散架构 (基于DiTs).
在AdaLN变压器中集成的模糊功能,用于统一的隐性表示和手势生成.

主要成果:

在三位一体,ZEGGS和BEAT数据集上,Persona-Gestor在最先进的方法上表现出优越的性能.
实现了高水平的手势-语音同步和自然性.
通过广泛的主观和客观评估来验证.

结论:

Persona-Gestor为语音驱动的手势合成设定了一个新的基准.
该模型显著提高了虚拟人类技术中的可用性和概括能力.
为更加沉浸式和交互式的虚拟人类体验铺平了道路.

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