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高真实性和高效率的说话肖像合成与细节感知神经辐射场
IEEE transactions on visualization and computer graphics
|October 31, 2024
概括
本研究介绍了HH-NeRF,这是一个新的框架,用于从音频中创建高分辨率,现实的说话肖像视频. 它通过将细节感知神经辐射场 (NeRF) 模块与高效超分辨率模块相结合,实现快速染和高保真度.
科学领域:
- 计算机视觉 计算机视觉
- 计算机图形 计算机图形
- 人工智能的人工智能
背景情况:
- 从音频中生成现实的说话肖像视频是计算机图形中的一个具有挑战性的任务.
- 现有的方法经常在高分辨率输出,快速染和捕捉像眼一样细微的细节方面扎.
研究的目的:
- 提出一种新的染框架,HH-NeRF,用于高保真,音频驱动的说话肖像视频生成.
- 为了实现高分辨率和快速染速度.
主要方法:
- 开发了一种细节感知神经辐射场 (NeRF) 模块,用于高保真低分辨率说话头的生成.
- 集成了一个高效的条件超分辨率模块,使用深度图和音频功能进行高分辨率视频合成.
- 在NeRF模块中使用编码的体积密度估计和音频眼感知颜色计算.
主要成果:
- HH-NeRF成功地生成了高分辨率 (900x900) 的说话肖像,提高了忠诚度和清晰度.
- 该框架有效地捕捉了自然的眼和高频细节.
- 实现了与以前快速方法相比的染时间,同时显著提高了输出质量.
结论:
- HH-NeRF在音频驱动的说话肖像视频生成方面取得了重大进展.
- 拟议的框架提供了一个强大的解决方案,用于创建现实的,高分辨率的数字化身.
- 该方法在视觉质量和细节保存方面优于最先进的技术.
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