MLUG:启动语言运动预训练,以实现统一的运动语言理解和生成
Hongliang Luo1, Wei Xi1, Daniel Tang2
1School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China.
本研究介绍了MLUG,这是一个用于从文本中生成人类运动的新框架. MLUG 通过视觉语言预训练来增强运动合成,以获得现实和情感表达的结果.
科学领域:
- 计算机视觉 计算机视觉
- 动画动画 动画动画
- 人工智能的人工智能
背景情况:
- 从文本中产生人类动作是具有挑战性的.
- 现有的模型在与开放的词汇和情感表达力作斗争.
研究的目的:
- 介绍MLUG,这是人类运动合成的新框架.
- 利用视觉语言预训练来改善运动生成.
主要方法:
- 使用带有运动文本对比损失的单模式编码器.
- 使用基于运动的文本编码器和解码器.
- 整合一个运动长度预测器,以实现准确的合成.
主要成果:
- MLUG有效地将文字描述与动态运动序列对齐.
- 在产生现实和多样化的动作方面表现出卓越的性能.
- 为开放词汇运动生成设定了一个新的基准.
结论:
- MLUG为微妙的人类运动合成提供了一个创新的解决方案.
- 该框架增强了情感表达力和身体可信性.
- MLUG代表了计算机视觉和动画方面的重大进步.
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