通过对应引导的3D一致多视角编辑
3D-Consistent Multi-View Editing by Correspondence Guidance
AI总结：
本文提出一种无需训练的引导框架，通过在图像编辑过程中强制多视角一致性，提升3D表示如NeRF或高斯点云模型的编辑一致性，实验表明其显著优于现有方法。
摘要：
最近扩散和流模型的进步大幅提升了基于文本的图像编辑，但独立编辑图像的方法常导致同一场景不同视角下的几何和光度不一致。此类不一致尤其影响3D表示如NeRF或高斯点云模型的编辑。我们提出一种无需训练的引导框架，在图像编辑过程中强制多视角一致性。关键思想是编辑后对应点应看起来相似。为此，我们引入一致性损失，引导去噪过程朝向一致的编辑。该框架灵活，可与各种图像编辑方法结合，支持密集和稀疏多视角编辑设置。实验结果表明，我们的方法显著提升了3D一致性，相比现有多视角编辑方法。我们还展示，这种增强的一致性能够实现高质量的高斯点云编辑，具有锐利的细节和对用户指定文本提示的强忠实性。请参阅我们的项目页面获取视频结果：https://3d-consistent-editing.github.io/
英文摘要：
Recent advancements in diffusion and flow models have greatly improved text-based image editing, yet methods that edit images independently often produce geometrically and photometrically inconsistent results across different views of the same scene. Such inconsistencies are particularly problematic for editing of 3D representations such as NeRFs or Gaussian splat models. We propose a training-free guidance framework that enforces multi-view consistency during the image editing process. The key idea is that corresponding points should look similar after editing. To achieve this, we introduce a consistency loss that guides the denoising process toward coherent edits. The framework is flexible and can be combined with widely varying image editing methods, supporting both dense and sparse multi-view editing setups. Experimental results show that our approach significantly improves 3D consistency compared to existing multi-view editing methods. We also show that this increased consistency enables high-quality Gaussian splat editing with sharp details and strong fidelity to user-specified text prompts. Please refer to our project page for video results: https://3d-consistent-editing.github.io/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
