感知指导的VR风格转移转移
概括
这项研究引入了一种新的VR风格传输模型 (VRST-GAN),通过优化虚拟现实内容中的色彩动态和时空一致性来增强沉浸感. 一个VR沉浸式预测器 (VR-IP) 在预测用户沉浸时达到91%的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 人与计算机的交互
- 感知计算是一种感知计算.
背景情况:
- 虚拟现实 (VR) 使用全向视频 (ODV) 提供沉浸式体验.
- 在VR中提高观众体验质量 (QoE) 需要根据感知因素操纵场景.
- 风格转移方法可以在VR中创建艺术效果,但它们对沉浸感的影响需要研究.
研究的目的:
- 调查影响虚拟现实中风格化沉浸感的感知因素.
- 开发一种感知导向的VR风格转移模型.
- 创建一个模型来预测VR内容的沉浸度.
主要方法:
- 引入了一个沉浸感应模型,用于发光度和色彩感知.
- 在风格化的VR内容中测量了色彩动态和时空一致性.
- 开发了VR风格传输GAN (VRST-GAN) 和VR沉浸性预测器 (VR-IP).
主要成果:
- 通过考虑色彩动态和时空一致性,VRST-GAN增强了VR沉浸力.
- VRST-GAN有效地将所需的风格转移到VR环境中.
- 虚拟现实沉浸度预测器 (VR-IP) 在预测用户沉浸度方面实现了91%的准确性.
结论:
- 诸如色彩动态和时空一致性之类的感知因素对于风格化的VR沉浸性至关重要.
- VRST-GAN提供了一种创新的方法来创建更沉浸式的VR内容.
- VR-IP模型证明了准确预测VR沉浸的可行性.
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