  Latent diffusion models for image generation have crossed a quality threshold
which enabled them to achieve mass adoption. Recently, a series of works have
made advancements towards replicating this success in the 3D domain,
introducing techniques such as point cloud VAE, triplane representation, neural
implicit surfaces and differentiable rendering based training. We take another
step along this direction, combining these developments in a two-step pipeline
consisting of 1) a triplane VAE which can learn latent representations of
textured meshes and 2) a conditional diffusion model which generates the
triplane features. For the first time this architecture allows conditional and
unconditional generation of high quality textured or untextured 3D meshes
across multiple diverse categories in a few seconds on a single GPU. It
outperforms previous work substantially on image-conditioned and unconditional
generation on mesh quality as well as texture generation. Furthermore, we
demonstrate the scalability of our model to large datasets for increased
quality and diversity. We will release our code and trained models.
