最多:通过时间剪辑Banzhaf互动的罕见文本的运动扩散模型.
IEEE transactions on visualization and computer graphics
|July 11, 2025
概括
通过临时剪辑Banzhaf交互的新型运动扩散模型MOST,通过罕见的语言提示增强了人类运动生成. 它使用细粒度的剪辑关系来精确地将文本与动作匹配,提高语义一致性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 从文本提示生成人类动作是一项挑战,尤其是在罕见的语言中.
- 现有的方法经常遭受粗的匹配,并忽略语义细节,由于运动冗余.
研究的目的:
- 通过临时剪辑班扎夫交互 (MOST) 引入一种新的运动扩散模型.
- 解决以往文本转移生成方法的局限性,特别是对于罕见的提示.
主要方法:
- MOST使用了一个采集阶段与时间剪辑Banzhaf交互来量化剪辑层面的文本-运动连贯性.
- 这使得细粒度的文字与动作剪辑相匹配,减少冗余.
- 在生成阶段的动作提示模块使用检索的剪辑来获得语义上一致的动作.
主要成果:
- 在文本转移检索和生成方面,MOST 实现了最先进的性能.
- 评估显示了显著的改进,特别是对于罕见和复杂的语言提示.
- 定量和定性结果证实了该模型在解决先前挑战方面的有效性.
结论:
- 大多数有效地从语言提示中产生人类运动,即使是罕见的提示.
- 新的时间片段Banzhaf互动显著改善了文字-动作连贯性,并减少了冗余.
- 这种方法推进了人工智能驱动的人类运动合成领域.
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