SealD-NeRF: Interactive Pixel-Level Editing for Dynamic Scenes by Neural Radiance Fields
This work addresses the need for flexible editing in implicit 3D models for tasks like scene post-processing and 3D content creation, but it is incremental as it extends prior methods to dynamic settings.
The paper tackles the problem of enabling pixel-level editing in dynamic scenes using Neural Radiance Fields (NeRF), which is limited in existing methods, and introduces SealD-NeRF to allow consistent edits across sequences by mapping actions to timeframes and using a teacher-student approach.
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.