Automatically generating high-quality real world 3D scenes is of enormous interest for applications such as virtual reality and robotics simulation. Towards this goal, we introduce NeuralField-LDM, a generative model capable of synthesizing complex 3D environments. We leverage Latent Diffusion Models that have been successfully utilized for efficient high-quality 2D content creation. We first train a scene auto-encoder to express a set of image and pose pairs as a neural field, represented as density and feature voxel grids that can be projected to produce novel views of the scene. To further compress this representation, we train a latent-autoencoder that maps the voxel grids to a set of latent representations. A hierarchical diffusion model is then fit to the latents to complete the scene generation pipeline. We achieve a substantial improvement over existing state-of-the-art scene generation models. Additionally, we show how NeuralField-LDM can be used for a variety of 3D content creation applications, including conditional scene generation, scene inpainting and scene style manipulation.
@article{arxiv.2304.09787,
title = {NeuralField-LDM: Scene Generation with Hierarchical Latent Diffusion Models},
author = {Seung Wook Kim and Bradley Brown and Kangxue Yin and Karsten Kreis and Katja Schwarz and Daiqing Li and Robin Rombach and Antonio Torralba and Sanja Fidler},
journal= {arXiv preprint arXiv:2304.09787},
year = {2023}
}