DreamFusion: Text-to-3D using 2D Diffusion. (arXiv:2209.14988v1 [cs.CV])

Recent breakthroughs in text-to-image synthesis have been driven by diffusion
models trained on billions of image-text pairs. Adapting this approach to 3D
synthesis would require large-scale datasets of labeled 3D data and efficient
architectures for denoising 3D data, neither of which currently exist. In this
work, we circumvent these limitations by using a pretrained 2D text-to-image
diffusion model to perform text-to-3D synthesis. We introduce a loss based on
probability density distillation that enables the use of a 2D diffusion model …

arxiv diffusion text