  Very recently neural implicit rendering techniques have been rapidly evolved
and shown great advantages in novel view synthesis and 3D scene reconstruction.
However, existing neural rendering methods for editing purposes offer limited
functionality, e.g., rigid transformation, or not applicable for fine-grained
editing for general objects from daily lives. In this paper, we present a novel
mesh-based representation by encoding the neural implicit field with
disentangled geometry and texture codes on mesh vertices, which facilitates a
set of editing functionalities, including mesh-guided geometry editing,
designated texture editing with texture swapping, filling and painting
operations. To this end, we develop several techniques including learnable sign
indicators to magnify spatial distinguishability of mesh-based representation,
distillation and fine-tuning mechanism to make a steady convergence, and the
spatial-aware optimization strategy to realize precise texture editing.
Extensive experiments and editing examples on both real and synthetic data
demonstrate the superiority of our method on representation quality and editing
ability. Code is available on the project webpage:
https://zju3dv.github.io/neumesh/.
