知识图增强交叉模式生成对抗网络,用于武术运动重建和遗产保护
1College of Physical Education, Nanjing Tech University, Nanjing, 211816, JiangSu, China. yuexiaoyu2025@163.com.
Scientific reports
|January 21, 2026
概括
本研究介绍了一个知识图增强的交叉模式生成对抗网络 (KG-CMGAN),以保护传统的武术. 这种新的方法重建复杂的运动,确保文化真实性和遗产保护的语义意义.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 文化遗产研究 文化遗产研究
背景情况:
- 传统的武术技术是复杂的和多维的,对数字保存提出了挑战.
- 现有的方法难以捕捉这些实践的物理执行和概念基础.
- 非物质文化遗产需要创新的方法来准确和真实的表现.
研究的目的:
- 为了呈现一个新的知识图增强交叉模式生成对抗网络 (KG-CMGAN) 保存传统的武术技巧.
- 开发一个端到端的框架,整合视觉,文本和序列数据来进行运动重建.
- 为了确保复制的武术运动的风格真实性和语义意义.
主要方法:
- 开发了一个全面的武术知识图表,正式化了特定领域的本体学.
- 实施了以知识为导向的跨模式对齐机制,以实现异质数据集成.
- 利用知识增强的对抗性学习架构来进行武术运动重建.
主要成果:
- 与最先进的基线相比,联合位置错误减少了28.4%.
- 达到了91.2%的知识一致性得分,表明了高的语义准确性.
- 在六种武术风格中,在维护风格真实性和概念基础方面取得了显著的改进.
结论:
- 知识图集成对于产生文化上真实的武术运动至关重要.
- KG-CMGAN框架为无形文化遗产的数字保存提供了一个强大的解决方案.
- 这项研究推进了捕捉传统实践的物理和概念方面的方法.
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