  Diffusion models have emerged as the best approach for generative modeling of
2D images. Part of their success is due to the possibility of training them on
millions if not billions of images with a stable learning objective. However,
extending these models to 3D remains difficult for two reasons. First, finding
a large quantity of 3D training data is much more complex than for 2D images.
Second, while it is conceptually trivial to extend the models to operate on 3D
rather than 2D grids, the associated cubic growth in memory and compute
complexity makes this infeasible. We address the first challenge by introducing
a new diffusion setup that can be trained, end-to-end, with only posed 2D
images for supervision; and the second challenge by proposing an image
formation model that decouples model memory from spatial memory. We evaluate
our method on real-world data, using the CO3D dataset which has not been used
to train 3D generative models before. We show that our diffusion models are
scalable, train robustly, and are competitive in terms of sample quality and
fidelity to existing approaches for 3D generative modeling.
