  Text-to-3D synthesis has recently emerged as a new approach to sampling 3D
models by adopting pretrained text-to-image models as guiding visual priors. An
intriguing but underexplored problem with existing text-to-3D methods is that
3D models obtained from the sampling-by-optimization procedure tend to have
mode collapses, and hence poor diversity in their results. In this paper, we
provide an analysis and identify potential causes of such a limited diversity,
and then devise a new method that considers the joint generation of different
3D models from the same text prompt, where we propose to use augmented text
prompts via textual inversion of reference images to diversify the joint
generation. We show that our method leads to improved diversity in text-to-3D
synthesis qualitatively and quantitatively.

Computer Vision and Pattern Recognition