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.

翻译：隐式神经表示（特别是神经辐射场NeRF）的广泛应用凸显了隐式三维模型编辑能力的迫切需求，这对于场景后处理和三维内容创建等任务至关重要。尽管NeRF编辑已有先前研究，但受限于编辑灵活性和质量，仍面临挑战。核心问题在于开发一种支持局部编辑以实现实时更新的神经表示。现有NeRF编辑方法虽能提供像素级调整或精细的几何与颜色修改，但大多局限于静态场景。本文提出SealD-NeRF，作为Seal-3D在动态环境下的像素级编辑扩展，专门针对D-NeRF网络设计。通过将编辑操作映射到特定时间帧、冻结负责动态场景表示的形变网络，并采用师生学习框架整合变更，本方法可实现跨序列的一致编辑。