Point-E
While recent work on text-conditional 3D object generation has shown promising results, the state-of-the-art methods typically require multiple GPU hours to produce a single sample. This contrasts with state-of-the-art generative image models, which make samples in several seconds or minutes. In this paper, we explore an alternative method for 3D object generation which makes 3D models in only 1-2 minutes on a single GPU. Our process first generates a single synthetic view using a text-to-image diffusion model. Then it produces a 3D point cloud using a second diffusion model, which conditions the generated image. While our method still needs to improve sample quality, it is one to two orders of magnitude faster to sample from, offering a practical trade-off for some use cases. We release our pre-trained point cloud diffusion models and evaluation code and models at this HTTPS URL. [2212.08751] Point-E: A System for Generating 3D Point Clouds from Complex Prompts