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The ability to generate highly realistic 2D images from mere text prompts hasrecently made huge progress in terms of speed and quality, thanks to the adventof image diffusion models. Naturally, the question arises if this can be alsoachieved in the generation of 3D content from such text prompts. To this end, anew line of methods recently emerged trying to harness diffusion models,trained on 2D images, for supervision of 3D model generation using viewdependent prompts. While achieving impressive results, these methods, however,have two major drawbacks. First, rather than commonly used 3D meshes, theyinstead generate neural radiance fields (NeRFs), making them impractical formost real applications. Second, these approaches tend to produce over-saturatedmodels, giving the output a cartoonish looking effect. Therefore, in this workwe propose a novel method for generation of highly realistic-looking 3D meshes.To this end, we extend NeRF to employ an SDF backbone, leading to improved 3Dmesh extraction. In addition, we propose a novel way to finetune the meshtexture, removing the effect of high saturation and improving the details ofthe output 3D mesh.

TextMesh: Generation of Realistic 3D Meshes From Text Prompts