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Text2Mesh: Text-Driven Neural Stylization for Meshes

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arxiv 2112.03221 v1 pith:W346UJUN submitted 2021-12-06 cs.CV cs.CLcs.GR

Oscar Michel , Roi Bar-On , Richard Liu , Sagie Benaim , Rana Hanocka This is my paper · ORCID
classification cs.CVcs.CLcs.GR
keywords meshstylemeshesneuraltext2meshnetworkprompttext
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 4 Pith papers

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    A meta-learning dissertation showing that distributed memory and hypernetworks can adapt to new tasks with few samples, applied to image classification, text-to-3D generation, and molecular binding prediction, with th...

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