LC-NeRF:在神经辐射场中的局部可控制的面部生成
IEEE transactions on visualization and computer graphics
|July 17, 2023
概括
LC-NeRF通过对口腔和鼻子等局部面部区域进行独立控制来增强3D面部生成. 这种新的方法提高了3D感知面部生成中的可控性和编辑效果.
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:
- 神经辐射场 (NeRF) 具有先进的3D面部生成,实现高视觉质量和一致性.
- 现有的基于NeRF的方法通常缺乏对特定面部区域的细粒度控制,限制了像嘴巴,鼻子或头发这样的特征的独立编辑.
研究的目的:
- 引入LC-NeRF,这是一个新的框架,旨在增强基于NeRF的3D面部生成的本地可控性.
- 为了能够精确地控制单个面部区域的几何和纹理.
主要方法:
- LC-NeRF使用局部区域发生器模块 (LRGM) 来将不同的面部区域建模为独立的NeRF.
- 空间意识融合模块 (SAFM) 将这些独立的NeRF集成到一个连贯的3D面部表示中.
- 修改与每个生成器相关的隐藏代码,可以对特定的面部区域进行有针对性的控制.
主要成果:
- 评估表明LC-NeRF与最先进的3D感知面部生成方法相比具有更高的局部可控性.
- 感知研究证实LC-NeRF在图像质量,面部一致性和编辑效率方面的优势.
- 该方法在下游应用中表现出强的性能,例如真实图像和文本驱动的面部编辑.
结论:
- 使用NeRF的LC-NeRF显著提高了使用NeRF的3D面部生成中的局部可控性.
- 拟议的架构提供了对面部几何和纹理的精确控制,在质量和编辑能力方面优于现有的方法.
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