# Text2Mesh: Text-Driven Neural Stylization for Meshes

**Authors:** Oscar Michel, Roi Bar-On, Richard Liu, Sagie Benaim, Rana Hanocka  
**arXiv:** [2112.03221](https://arxiv.org/abs/2112.03221)  
**Date:** 2021-12-06  
**Summary page:** https://www.summarizepaper.com/fr/paper/2112.03221v1/

## Abstract

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.