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DreamFusion: Text-to-3D using 2D Diffusion. (arXiv:2209.14988v1 [cs.CV])
Sept. 30, 2022, 1:12 a.m. | Ben Poole, Ajay Jain, Jonathan T. Barron, Ben Mildenhall
cs.LG updates on arXiv.org arxiv.org
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 …
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