Recently, the editing of neural radiance fields (NeRFs) has gained considerable attention, but most prior works focus on static scenes while research on the appearance editing of dynamic scenes is relatively lacking. In this paper, we propose a novel framework to edit the local appearance of dynamic NeRFs by manipulating pixels in a single frame of training video. Specifically, to locally edit the appearance of dynamic NeRFs while preserving unedited regions, we introduce a local surface representation of the edited region, which can be inserted into and rendered along with the original NeRF and warped to arbitrary other frames through a learned invertible motion representation network. By employing our method, users without professional expertise can easily add desired content to the appearance of a dynamic scene. We extensively evaluate our approach on various scenes and show that our approach achieves spatially and temporally consistent editing results. Notably, our approach is versatile and applicable to different variants of dynamic NeRF representations.

翻译：近年来，神经辐射场（NeRF）的编辑问题引起了广泛关注，但现有工作大多聚焦于静态场景，对动态场景的外观编辑研究相对缺乏。本文提出一种新颖框架，通过操作训练视频中单帧的像素点，实现对动态NeRF的局部外观编辑。具体而言，为在保留下未编辑区域的前提下局部编辑动态NeRF的外观，我们引入编辑区域的局部表面表征。该表征可嵌入原始NeRF中与其共同渲染，并通过学习的可逆运动表征网络变形到其他任意帧。采用本方法后，非专业用户也能轻松为动态场景外观添加所需内容。我们在多种场景上进行了充分评估，表明本方法可实现时空一致的编辑结果。值得注意的是，本方法具有通用性，可适用于不同变体的动态NeRF表征。