NeRF-Art:文本驱动的神经辐射场的造型化
概括
NeRF-Art允许通过神经辐射场 (NeRF) 代表的3D场景进行文本引导的风格化. 这种方法同时改变了外观和几何,而不需要3D网格,提供了更好的细节和一致性.
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) 在新的视图合成方面表现出色,但风格化,特别是以文本为指导,仍然很困难.
- 现有的方法难以同时改变外观和几何形状,或者需要网格指导.
研究的目的:
- 引入NeRF-Art,这是一个新的以文本为导向的方法,用于风格化预训练的NeRF模型.
- 基于文本提示,在NeRF中实现对外观和几何的同时操作.
主要方法:
- 全球-本地对比的学习策略与风格控制的方向约束相结合.
- 重量规范化以减轻密度场转换期间的模糊效应和几何噪声等人工物.
主要成果:
- NeRF-Art成功地使用文本提示符对NeRF模型进行了风格化,改变了几何和外观.
- 该方法可以实现高质量的造型,而不依赖3D网格.
- 在单视图风格化质量和跨视图一致性方面表现出有效性和稳定性.
结论:
- NeRF-Art为文本引导的3D场景造型提供了强大而有效的解决方案.
- 该方法克服了以前方法的局限性,因为它可以在没有网格指导的情况下同时改变几何和外观.
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