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The technique involves starting with a random 3D representation, rendering a 2D view of a desired object from a random camera angle, adding noise to that image, denoising it with a diffusion model, then optimizing the random 3D representation so it matches the denoised image.

In the end, they were able to use an off-the-shelf, pretrained image diffusion model to create smooth, realistic-looking 3D shapes without the need for costly retraining.

Because their method relies on a pretrained diffusion model, it inherits the biases and shortcomings of that model, making it prone to hallucinations and other failures.

Improving the underlying diffusion model would enhance their process.

This Conditional Variational Diffusion Model produces state-of-the-art results also surpassing the m-rBCR model presented here.