SealD-NeRF: Interactive Pixel-Level Editing for Dynamic Scenes by Neural Radiance Fields
<|reference_start|>SealD-NeRF: Interactive Pixel-Level Editing for Dynamic Scenes by Neural Radiance Fields: 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.<|reference_end|>
@article{huang2024seald-nerf:,
    title={SealD-NeRF: Interactive Pixel-Level Editing for Dynamic Scenes by Neural
  Radiance Fields},
    author={Zhentao Huang, Yukun Shi, Neil Bruce, Minglun Gong},
    journal={arXiv preprint arXiv:2402.13510},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.13510},
    primaryClass={cs.CV}
}