Text2Mesh: Text-Driven Neural Stylization for Meshes [article]

Oscar Michel, Roi Bar-On, Richard Liu, Sagie Benaim, Rana Hanocka
2021 arXiv   pre-print
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
more » ... h 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.
arXiv:2112.03221v1 fatcat:2mfgjh37lna5hnjj6pvms6zuey