人工智能下的民间体育文化的智能发展和保护
Zhihui Li1,2, Shamsulariffin Bin Samsudin3, Noor Hamzani Farizan4
1Department of Sports Studies, Faculty of Educational Studies, Universiti Putra Malaysia, Serdang, 43400, Selangor, Malaysia.
Scientific reports
|April 24, 2025
概括
本研究介绍了一个CycleGAN模型,用于生成传统体育运动的现实人体图像,以保护文化. 该模型增强了图像质量和现实性,有助于民间体育文化的数字化发展.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 数字遗产是数字遗产的一部分.
背景情况:
- 民间体育文化在数字保存和智能发展方面面临着挑战.
- 高质量的人类形象生成对于重现传统运动运动至关重要.
研究的目的:
- 提出一个循环一致的生成对抗网络 (CycleGAN) 模型,用于生成传统体育运动的现实的人类图像.
- 通过姿势,身份和外观一致性机制来提高模型的表现.
- 为数字化保存和发展民间体育文化提供技术支持.
主要方法:
- 使用一个CycleGAN框架来生成图像.
- 纳入了对姿势和身份一致性的歧视机制.
- 引入了一个外观一致性损失函数.
- 在DeepFashion和Market-1501数据集上验证了模型.
主要成果:
- 与现有模型相比,拟议的模型实现了卓越的视觉质量和现实主义.
- 外观一致性损失改善了图像开始得分 (1.49%),结构相似性指数 (1.76%) 和多样性得分 (2.2%).
- 生成的图像有58.25%的概率被认为是真实的,优于其他模型.
- 在平衡民间体育文化活动的形象多样性和质量方面表现出卓越.
结论:
- 循环GAN模型有效地重现了民间运动运动的细节和现实主义.
- 开发的模型为数字化保存和发展传统体育提供了重要的技术支持.
- 这些发现凸显了该模型在通过先进的AI技术增强文化遗产方面的潜力.
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