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.

翻译：摘要：神经辐射场（NeRF）显著推动了高逼真度与高表现力三维场景的生成技术。然而，对NeRF进行编辑（尤其是几何形态修改）仍是一项重大挑战，这一问题阻碍了NeRF在各类应用中的广泛采用。为解决神经隐式场的高效编辑难题，我们提出"神经替身"（Neural Impostor）——一种结合显式四面体网格与多网格隐式场的混合表示方法，其中每个四面体对应一个专属多网格隐式场。通过引入多网格重心坐标编码，本框架实现了显式形状操控与隐式场几何编辑的桥接，为在保持复杂体表外观的同时对神经隐式场进行形变、组合与生成提供了实用方案。此外，我们提出了一套基于显式几何编辑操作的完整神经隐式场编辑流程。通过涵盖合成对象与真实采集数据的多样示例与实验，验证了本系统的鲁棒性与适应性。最后，我们演示了利用多种编辑操作对混合合成-采集对象进行创作的全过程，凸显了"神经替身"在三维内容创作与操控领域的变革潜力。