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SealD-NeRF: Interactive Pixel-Level Editing for Dynamic Scenes by Neural Radiance Fields (2402.13510v1)

Published 21 Feb 2024 in cs.CV

Abstract: 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.

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Authors (4)
  1. Zhentao Huang (15 papers)
  2. Yukun Shi (8 papers)
  3. Neil Bruce (4 papers)
  4. Minglun Gong (33 papers)
Citations (2)
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