Text2Mesh: Text-Driven Neural Stylization for Meshes

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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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Neural Stylization Meshes Text2Mesh Mean Opinion Score (Q1:Overall) 3.9 ± 0.37 # 1
Mean Opinion Score (Q2: Content) 4.04 ± 0.53 # 1
Mean Opinion Score (Q3: Style) 3.91 ± 0.51 # 1
Neural Stylization Meshes VQGAN Mean Opinion Score (Q1:Overall) 2.83 ± 0.39 # 2
Mean Opinion Score (Q2: Content) 3.6 ± 0.68 # 2
Mean Opinion Score (Q3: Style) 2.59 ± 0.44 # 2