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扩散:使用消化和融合方案的灵活风格化运动生成.
IEEE transactions on visualization and computer graphics
|October 13, 2025
概括
通过使用双条件扩散模型,DiFusion通过文本控制和风格适应来产生多样化的人类运动. 这种框架增强了动作合成中的现实主义和多样性,使风格插曲等应用程序成为可能.
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:
- 现有的以文本为导向的人类运动合成方法与风格表达斗争.
- 通过各种方式控制运动风格仍然是一个挑战.
研究的目的:
- 提出DiFusion,一个用于多样化风格的人类运动生成的新框架.
- 通过多种方式 (文本标签或动作序列) 通过文本和风格对动作内容进行灵活控制.
主要方法:
- 采用双条件运动隐藏扩散模型,用于独立的内容和风格控制.
- 介绍了消化和融合培训计划,以处理不平衡的数据集复杂性.
- 消化特定领域的知识,并适应地将其融合为兼容的培训.
主要成果:
- 在内容对齐,风格表现力,现实主义和多样性方面表现出比现有方法更好的表现.
- 综合评估验证了DiFusion框架的有效性.
- 该方法显示了实际应用的潜力,例如运动样式插值.
结论:
- DiFusion提供了一个强大的解决方案,用于风格化的人类运动合成,具有灵活的多模式风格控制.
- 消化和融合培训计划有效地解决了数据集不平衡问题.
- 该框架显著推进了人类运动生成的最先进技术.
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