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

NeuMesh: Learning Disentangled Neural Mesh-based Implicit Field for Geometry and Texture Editing