Submitted by RepresentativeCod613 t3_zrfy75 in MachineLearning
It's only been a month since OpenAI released ChatGPT, and yesterday they launched Point-E, a new Dalle-like model that generates 3D Point Clouds from Complex Prompts. As someone who is always interested in the latest advancements in machine learning, I was really excited to dig into this paper and see what it had to offer.
One of the key features of Point-E is its use of diffusion models to generate synthetic views and 3D point clouds. These models use text input to generate an image, which is then used as a reference for generating the 3D point cloud. This process takes only 1-2 minutes on a single GPU, making it much faster than previous state-of-the-art methods.
While the quality of the samples produced by Point-E may be lower than those produced by other methods, the speed of generation makes it a practical option for certain use cases.
If you're interested in learning more about this new model and how it was developed, I highly recommend giving the full paper a read. But if you're more into reading the gist of it, I added a link to an overview blog I published about.
The blog: https://dagshub.com/blog/point-e/
The paper: https://arxiv.org/abs/2212.08751
I'm sure I have yet to reach all the insights while writing the blog, and I'd love to get your thoughts about the model and how OpenAI developed it.
master3243 t1_j152mmj wrote
The abstract puts this project into perspective, their methods are much faster but still doesn't beat the state of the art.
> 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