The ability to generate highly realistic 2D images from mere text prompts has
recently made huge progress in terms of speed and quality, thanks to the advent
of image diffusion models. Naturally, the question arises if this can be also
achieved in the generation of 3D content from such text prompts. To this end, a
new line of methods recently emerged trying to harness diffusion models,
trained on 2D images, for supervision of 3D model generation using view
dependent prompts. While achieving impressive results, these methods, however,
have two major drawbacks. First, rather than commonly used 3D meshes, they
instead generate neural radiance fields (NeRFs), making them impractical for
most real applications. Second, these approaches tend to produce over-saturated
models, giving the output a cartoonish looking effect. Therefore, in this work
we 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 3D
mesh extraction. In addition, we propose a novel way to finetune the mesh
texture, removing the effect of high saturation and improving the details of
the output 3D mesh.

通过引入 SDF backbon，扩展 NeRF 来进行 3D 模型生成，并通过新颖的方法对网格纹理进行微调以改善输出的细节。