Dyn-E: Local Appearance Editing of Dynamic Neural Radiance Fields
Shangzhan Zhang Sida Peng Yinji ShenTu Qing Shuai Tianrun Chen Kaicheng Yu Hujun Bao Xiaowei Zhou
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.