在二次对话中使用扩散模型生成交互手势
Yuya Okadome1,2, Yazan Alkatshah2, Yutaka Nakamura2,3
1Faculty of Engineering, Tokyo University of Science, Katsushika, Tokyo, Japan.
PloS one
|December 30, 2025
概括
本研究介绍了一种交互扩散模型 (IDM),用于在人机交互中产生自然的对话行为. 该模型为化身和机器人创造了类似人类的动作,增强了对话流性.
科学领域:
- 人工智能的人工智能
- 计算机图形 计算机图形
- 人与计算机的交互
背景情况:
- 对计算机图形 (CG) 化身和对话机器人的期望越来越高,需要提高对话技巧.
- 当前的运动生成模型往往忽略了对话伙伴之间的关键交互因素.
- 自然的对话动作,如点头,需要考虑他人的行为和声音.
研究的目的:
- 为自主对话系统开发一种自动交互运动生成系统.
- 通过多式联络对话技能,增强流利的人与计算机的互动.
- 为了产生自然的,类似人类的交互行为,考虑二元对话动态.
主要方法:
- 实施了一种交互扩散模型 (IDM),利用扩散方法和掩盖功能.
- 在IDM中,两个参与者进行双向对话,使用面具来生成条件输入.
- 该模型容纳了缺少的功能,并使预测无需再培训.
主要成果:
- IDM成功地为二元对话生成交互行为.
- 实验结果表明,模型在对话中产生类似人类的行为.
- 在30毫秒内实现了类似人类行为的生成.
结论:
- 开发的交互扩散模型 (IDM) 有效地产生自然的对话动作.
- 这种方法显著提高了与CG化身和机器人的交互的流性和人与人相似性.
- IDM为推进自主对话系统提供了一个有前途的解决方案.
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