Dyn-E: Local Appearance Editing of Dynamic Neural Radiance Fields
This enables non-experts to edit dynamic scenes, addressing a gap in prior work focused on static scenes, though it is incremental as it builds on existing dynamic NeRF methods.
The paper tackles the problem of editing local appearance in dynamic neural radiance fields (NeRFs) by allowing users to manipulate pixels in a single training video frame, achieving spatially and temporally consistent results across various scenes.
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.