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.

本文介绍了 SealD-NeRF，它是 Seal-3D 的一个扩展，用于在动态环境中进行像素级编辑，特别针对 D-NeRF 网络。它通过将编辑操作映射到特定的时间框架，冻结负责动态场景表示的变形网络，并使用师生方法来集成更改，实现序列中的一致编辑。

SealD-NeRF: 动态场景的神经辐射场交互式像素级编辑

SealD-NeRF: Interactive Pixel-Level Editing for Dynamic Scenes by Neural  Radiance Fields

Evaluation of text generation to date has primarily focused on content
created sequentially, rather than improvements on a piece of text. Writing,
however, is naturally an iterative and incremental process that requires
expertise in different modular skills such as fixing outdated information or
making the style more consistent. Even so, comprehensive evaluation of a
model's capacity to perform these skills and the ability to edit remains
sparse. This work presents EditEval: An instruction-based, benchmark and
evaluation suite that leverages high-quality existing and new datasets for
automatic evaluation of editing capabilities such as making text more cohesive
and paraphrasing. We evaluate several pre-trained models, which shows that
InstructGPT and PEER perform the best, but that most baselines fall below the
supervised SOTA, particularly when neutralizing and updating information. Our
analysis also shows that commonly used metrics for editing tasks do not always
correlate well, and that optimization for prompts with the highest performance
does not necessarily entail the strongest robustness to different models.
Through the release of this benchmark and a publicly available leaderboard
challenge, we hope to unlock future research in developing models capable of
iterative and more controllable editing.

本文提出 EditEval：一种以指令为基础的评测套件，利用高质量的现有和新数据集自动评估编辑能力，证明了 InstructGPT 和 PEER 的表现最佳，而大多数基线都低于监督学习 SOTA，本文通过这个基准的发布和公开可用的排行榜挑战，希望能够解锁未来发展能够进行迭代和更可控编辑的模型的研究。