  We present a latent diffusion model over 3D scenes, that can be trained using
only 2D image data. To achieve this, we first design an autoencoder that maps
multi-view images to 3D Gaussian splats, and simultaneously builds a compressed
latent representation of these splats. Then, we train a multi-view diffusion
model over the latent space to learn an efficient generative model. This
pipeline does not require object masks nor depths, and is suitable for complex
scenes with arbitrary camera positions. We conduct careful experiments on two
large-scale datasets of complex real-world scenes -- MVImgNet and
RealEstate10K. We show that our approach enables generating 3D scenes in as
little as 0.2 seconds, either from scratch, from a single input view, or from
sparse input views. It produces diverse and high-quality results while running
an order of magnitude faster than non-latent diffusion models and earlier
NeRF-based generative models
