Neural Impostor: Editing Neural Radiance Fields with Explicit Shape
  Manipulation
Ruiyang Liu Jinxu Xiang Bowen Zhao Ran Zhang Jingyi Yu Changxi Zheng
Neural Radiance Fields (NeRF) have significantly advanced the generation of
highly realistic and expressive 3D scenes. However, the task of editing NeRF,
particularly in terms of geometry modification, poses a significant challenge.
This issue has obstructed NeRF's wider adoption across various applications. To
tackle the problem of efficiently editing neural implicit fields, we introduce
Neural Impostor, a hybrid representation incorporating an explicit tetrahedral
mesh alongside a multigrid implicit field designated for each tetrahedron
within the explicit mesh. Our framework bridges the explicit shape manipulation
and the geometric editing of implicit fields by utilizing multigrid barycentric
coordinate encoding, thus offering a pragmatic solution to deform, composite,
and generate neural implicit fields while maintaining a complex volumetric
appearance. Furthermore, we propose a comprehensive pipeline for editing neural
implicit fields based on a set of explicit geometric editing operations. We
show the robustness and adaptability of our system through diverse examples and
experiments, including the editing of both synthetic objects and real captured
data. Finally, we demonstrate the authoring process of a hybrid
synthetic-captured object utilizing a variety of editing operations,
underlining the transformative potential of Neural Impostor in the field of 3D
content creation and manipulation.