SealD-NeRF：基于神经辐射场的动态场景交互式像素级编辑
SealD-NeRF: Interactive Pixel-Level Editing for Dynamic Scenes by Neural Radiance Fields
AI总结：
本文提出SealD-NeRF，将Seal-3D扩展到动态场景，通过映射编辑操作到特定时间帧、冻结变形网络并结合教师-学生方法，实现D-NeRF上的像素级一致编辑。
摘要：
隐式神经表示的广泛采用，尤其是神经辐射场（NeRF），凸显了对隐式3D模型编辑能力日益增长的需求，这对于场景后处理和3D内容创建等任务至关重要。尽管此前在NeRF编辑方面已有诸多努力，但由于编辑灵活性和质量的限制，挑战依然存在。关键问题在于开发一种支持局部编辑以实现实时更新的神经表示。当前的NeRF编辑方法提供像素级调整或详细的几何与颜色修改，但大多局限于静态场景。本文提出了SealD-NeRF，这是Seal-3D在动态场景下像素级编辑的扩展，专门针对D-NeRF网络。它通过将编辑操作映射到特定时间帧、冻结负责动态场景表示的变形网络，并采用教师-学生方法来整合更改，从而在序列间实现一致的编辑。
英文摘要：
The widespread adoption of implicit neural representations, especially Neural Radiance Fields (NeRF), highlights a growing need for editing capabilities in implicit 3D models, essential for tasks like scene post-processing and 3D content creation. Despite previous efforts in NeRF editing, challenges remain due to limitations in editing flexibility and quality. The key issue is developing a neural representation that supports local edits for real-time updates. Current NeRF editing methods, offering pixel-level adjustments or detailed geometry and color modifications, are mostly limited to static scenes. This paper introduces SealD-NeRF, an extension of Seal-3D for pixel-level editing in dynamic settings, specifically targeting the D-NeRF network. It allows for consistent edits across sequences by mapping editing actions to a specific timeframe, freezing the deformation network responsible for dynamic scene representation, and using a teacher-student approach to integrate changes.
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。
