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Latent diffusion models for image generation have crossed a quality thresholdwhich enabled them to achieve mass adoption. Recently, a series of works havemade advancements towards replicating this success in the 3D domain,introducing techniques such as point cloud VAE, triplane representation, neuralimplicit surfaces and differentiable rendering based training. We take anotherstep along this direction, combining these developments in a two-step pipelineconsisting of 1) a triplane VAE which can learn latent representations oftextured meshes and 2) a conditional diffusion model which generates thetriplane features. For the first time this architecture allows conditional andunconditional generation of high quality textured or untextured 3D meshesacross multiple diverse categories in a few seconds on a single GPU. Itoutperforms previous work substantially on image-conditioned and unconditionalgeneration on mesh quality as well as texture generation. Furthermore, wedemonstrate the scalability of our model to large datasets for increasedquality and diversity. We will release our code and trained models.

3DGen: Triplane Latent Diffusion for Textured Mesh Generation