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Creation of 3D content by stylization is a promising yet challenging problemin computer vision and graphics research. In this work, we focus on stylizingphotorealistic appearance renderings of a given surface mesh of arbitrarytopology. Motivated by the recent surge of cross-modal supervision of theContrastive Language-Image Pre-training (CLIP) model, we propose TANGO, whichtransfers the appearance style of a given 3D shape according to a text promptin a photorealistic manner. Technically, we propose to disentangle theappearance style as the spatially varying bidirectional reflectancedistribution function, the local geometric variation, and the lightingcondition, which are jointly optimized, via supervision of the CLIP loss, by aspherical Gaussians based differentiable renderer. As such, TANGO enablesphotorealistic 3D style transfer by automatically predicting reflectanceeffects even for bare, low-quality meshes, without training on a task-specificdataset. Extensive experiments show that TANGO outperforms existing methods oftext-driven 3D style transfer in terms of photorealistic quality, consistencyof 3D geometry, and robustness when stylizing low-quality meshes. Our codes andresults are available at our project webpage https://cyw-3d.github.io/tango/.

TANGO: Text-driven Photorealistic and Robust 3D Stylization via Lighting Decomposition