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产品生物形象形式的智能生成通过多式联络情感加权量化量化
Xinran Chen1, Li Lin1,2, Mingqing Yang3
1School of Mechanical Engineering, Guizhou University, Guiyang, 550025, China.
Scientific reports
|January 26, 2026
概括
本研究介绍了一种基于数据的方法,用于使用StyleGAN生成生物图像,改善AI设计中的情感共. 该方法增强了用户偏好分析和可控性,以获得更个性化和不那么均的输出.
科学领域:
- 设计科学设计科学
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 传统的生物图像设计缺乏定量用户偏好分析和AI可控性.
- 人工智能驱动的生成往往导致同质的输出缺乏情感深度.
研究的目的:
- 建议使用StyleGAN.使用数据驱动的生物图像生成方法 (DL-BBI).
- 解决AI图像生成中用户偏好分析和可控性的局限性.
- 在生物产品设计中增强情感共.
主要方法:
- 进行了多模态情感认知实验,整合了视觉语言表达数据和情感突出性.
- 开发了一种情感加权量化方法,用于处理用户情感偏好数据.
- 应用的StyleGAN潜伏空间线性插曲用于可控制的产品生物特征的融合.
主要成果:
- 通过使用伴侣机器人的例子验证了该方法的可行性和优势.
- 证明有效增强设计解决方案和用户之间的情感共.
- 在生成的设计解决方案的"友好"评分中实现了22.6%的改进.
结论:
- 拟议的DL-BBI方法能够智能生成具有更好的情感连接的生物产品形式.
- 数据驱动的方法克服了传统方法的局限性,并提高了AI设计的可控性.
- 这种技术为创建更加以用户为中心和情感共的生物设计提供了有希望的方向.
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