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.

本文介绍了 SealD-NeRF，它是 Seal-3D 的一个扩展，用于在动态环境中进行像素级编辑，特别针对 D-NeRF 网络。它通过将编辑操作映射到特定的时间框架，冻结负责动态场景表示的变形网络，并使用师生方法来集成更改，实现序列中的一致编辑。