“Point·E: A System for Generating 3D Point Clouds from Complex Prompts”, Alex Nichol, Heewoo Jun, Prafulla Dhariwal, Pamela Mishkin, Mark Chen2022-12-16 (diffusion model)⁠:

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 [GLIDE], 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 Github.

…8. Acknowledgments: We would like to thank everyone behind ChatGPT for creating a tool that helped provide useful writing feedback.