相关实验视频
Updated: Mar 1, 2026

06:54
Photorealistic Learned Landscapes for Augmented Reality
Published on: June 27, 2025
815
通过文本提示符编辑3D场景,无需重新训练.
IEEE transactions on visualization and computer graphics
|February 27, 2026
概括
本研究介绍了DN2N,一种新的以文本驱动的3D场景编辑方法. DN2N允许使用2D图像编辑技术实现多功能3D场景修改,无需重新训练,解决多视图一致性挑战.
科学领域:
- 计算机视觉 计算机视觉
- 3D 图形 3D 图形
- 人工智能的人工智能
背景情况:
- 扩散模型具有先进的二维图像合成和编辑.
- 将这些扩展到3D场景编辑方面面临着场景表示和多视图一致性的挑战.
- 现有的方法通常需要特定于场景的模块,并为每个编辑任务重新培训.
研究的目的:
- 提出一个多功能文本驱动的3D场景编辑方法 (DN2N).
- 为了实现直接编辑3D场景,而无需重新训练模型.
- 解决文本驱动的3D场景编辑中的多视图一致性问题.
主要方法:
- 在3D场景的多视图图像上使用现成的基于2D文本的编辑模型.
- 应用内容过以保持3D一致性.
- 开发了一种多功能的神经辐射场 (NeRF) 模型结构,具有新的交叉视图规范化术语,以减轻干扰.
主要成果:
- 通过文本提示显示多种编辑类型,包括外观更改,天气转换,对象替换和风格转移.
- 在没有特定于场景或编辑类型的模型定制或再培训的情况下实现多功能编辑功能.
- 显示编辑时间与现有的基于3D高斯分片 (3DGS) 的方法相比较,提高了实际适用性.
结论:
- DN2N提供了一种多功能和高效的解决方案,用于基于文本的3D场景编辑.
- 该方法克服了现有方法的局限性,消除了再培训和专业模块的需要.
- DN2N通过其广泛的适用性和可比性能来增强3D场景编辑的实际价值.
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