Text2Mesh: Text-Driven Neural Stylization for Meshes

Autor:	Michel, Oscar, Bar-On, Roi, Liu, Richard, Benaim, Sagie, Hanocka, Rana
Rok vydání:	2021
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Computation and Language Computer Science - Graphics
Druh dokumentu:	Working Paper
Popis:	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. Comment: project page: https://threedle.github.io/text2mesh/
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2112.03221 Zobrazit plný text záznamu View this record from Arxiv