Point-E: A System for Generating 3D Point Clouds from Complex Prompts

TL;DR

Point-E introduces a fast method for generating 3D point clouds from complex prompts by combining text-to-image and image-to-3D diffusion models, significantly reducing generation time compared to previous approaches.

Contribution

The paper presents a novel two-step diffusion-based approach for 3D point cloud generation that is much faster than existing methods, enabling practical real-time applications.

Findings

Generates 3D point clouds in 1-2 minutes on a single GPU

Offers a practical trade-off with slightly lower quality than state-of-the-art methods

Provides open-source models and evaluation tools for the community

Abstract

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 is in stark contrast to state-of-the-art generative image models, which produce samples in a number of seconds or minutes. In this paper, we explore an alternative method for 3D object generation which produces 3D models in only 1-2 minutes on a single GPU. Our method first generates a single synthetic view using a text-to-image diffusion model, and then produces a 3D point cloud using a second diffusion model which conditions on the generated image. While our method still falls short of the state-of-the-art in terms of 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, as well as evaluation code and models, at https://github.com/openai/point-e.