SealD-NeRF: Interactive Pixel-Level Editing for Dynamic Scenes by Neural
  Radiance Fields
Zhentao Huang Yukun Shi Neil Bruce Minglun Gong
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.