In this work, we develop intuitive controls for editing the style of 3D
objects. Our framework, Text2Mesh, stylizes a 3D mesh by predicting color and
local geometric details which conform to a target text prompt. We consider a
disentangled representation of a 3D object using a fixed mesh input (content)
coupled with a learned neural network, which we term neural style field
network. In order to modify style, we obtain a similarity score between a text
prompt (describing style) and a stylized mesh by harnessing the
representational power of CLIP. Text2Mesh requires neither a pre-trained
generative model nor a specialized 3D mesh dataset. It can handle low-quality
meshes (non-manifold, boundaries, etc.) with arbitrary genus, and does not
require UV parameterization. We demonstrate the ability of our technique to
synthesize a myriad of styles over a wide variety of 3D meshes.

本文提出 Text2Mesh 算法，通过基于神经网络的颜色预测和本地几何细节预测实现对三维网格的样式编辑，使用 disentangled 表示法来对三维物体进行处理，无需预先训练的生成模型或特殊三维网格数据集，可以合成各种样式的三维网格。