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Recent progress in the text-driven 3D stylization of a single object has beenconsiderably promoted by CLIP-based methods. However, the stylization ofmulti-object 3D scenes is still impeded in that the image-text pairs used forpre-training CLIP mostly consist of an object. Meanwhile, the local details ofmultiple objects may be susceptible to omission due to the existing supervisionmanner primarily relying on coarse-grained contrast of image-text pairs. Toovercome these challenges, we present a novel framework, dubbed TeMO, to parsemulti-object 3D scenes and edit their styles under the contrast supervision atmultiple levels. We first propose a Decoupled Graph Attention (DGA) module todistinguishably reinforce the features of 3D surface points. Particularly, across-modal graph is constructed to align the object points accurately and nounphrases decoupled from the 3D mesh and textual description. Then, we develop aCross-Grained Contrast (CGC) supervision system, where a fine-grained lossbetween the words in the textual description and the randomly rendered imagesare constructed to complement the coarse-grained loss. Extensive experimentsshow that our method can synthesize high-quality stylized content andoutperform the existing methods over a wide range of multi-object 3D meshes.Our code and results will be made publicly available

TeMO: Towards Text-Driven 3D Stylization for Multi-Object Meshes