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Sign2Story:通过智能手机传感器实现近乎实时的手动手势的多模式框架,用于人工智能生成的音频漫画

Gul Faraz¹, Lei Jing¹, Xiang Li¹

¹Graduate School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Japan.

Sensors (Basel, Switzerland)

|January 28, 2026

概括

这项研究引入了一种新的多式联通框架,使用人工智能和智能手机传感器通过手势从新闻头条生成音频漫画. 这种创新方法提供了一个替代的输入方法,提高了传统交互挑战的用户的可访问性.

科学领域:

人与计算机的交互
人工智能的人工智能
多式联运系统多式联运系统

背景情况:

像触摸和语音这样的传统输入方法对一些用户来说可能具有挑战性.
生成型人工智能和多式人工智能为人机交互提供了新的可能性.
新闻标题可以转化为有吸引力的内容格式.

研究的目的:

开发一个多式模式框架,使用智能手机运动传感器和生成人工智能从实时新闻头条创建音频漫画.
探索基于手势的输入作为触摸或语音交互的替代品.
研究大型语言模型 (LLM) 和多式人工智能的潜力,用于超越漫画世代的各种应用.

主要方法:

利用智能手机的运动传感器进行手势识别 (手-波手势).
采用生成性AI,包括LLaVA,Gemma和Qwen等大型语言模型 (LLM),用于创建内容.
集成了一个AI-in-the-loop机制,用于代性输出改进.
整合了人工智能生成的音频叙述,提供了沉浸式体验.

主要成果:

通过使用手势,成功地从与技术相关的新闻头条生成了音频漫画.
与Qwen3-VL.L.相比,LLaVA在生成与小组结合的故事方面表现出更好的表现.
该框架显示了用于不同手势映射到不同的AI任务的可扩展性.
人工智能在循环中反复地改善了输出质量,而不需要人类干预.

结论:

拟议的框架为内容创建和人机交互提供了一种可行的和可访问的方法.
基于手势的输入与生成AI相结合,为面对传统输入挑战的用户提供了一个有希望的替代方案.
该系统的模块化设计允许与各种多式联络AI应用程序集成.

关键词:

物联网的物联网,就是物联网.可访问性可访问性身体区域网络身体区域网络漫画漫画是一个漫画.生成型的人工智能陀螺仪传感器陀螺仪传感器大型语言模型 (LLM)多式多样化的多式模式一个声音,一个声音,一个声音.

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