Summary of Sampling 3d Gaussian Scenes in Seconds with Latent Diffusion Models, by Paul Henderson et al.
Sampling 3D Gaussian Scenes in Seconds with Latent Diffusion Models
by Paul Henderson, Melonie de Almeida, Daniela Ivanova, Titas Anciukevičius
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed latent diffusion model is capable of generating 3D scenes using only 2D image data, with no requirement for object masks or depth information. The approach involves designing an autoencoder that maps multi-view images to 3D Gaussian splats and simultaneously building a compressed latent representation. A multi-view diffusion model is then trained over the latent space to learn an efficient generative model. This pipeline enables generating 3D scenes in as little as 0.2 seconds, either from scratch, from a single input view, or from sparse input views. The results are diverse and high-quality, outperforming non-latent diffusion models and earlier NeRF-based generative models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having the power to create 3D scenes using only 2D images! This amazing new technology allows you to do just that. It works by taking lots of pictures from different angles and then using a special kind of computer program to combine them into a single 3D scene. The best part is that it can be used to make all sorts of things, like videos or even entire movies. |
Keywords
» Artificial intelligence » Autoencoder » Diffusion model » Generative model » Latent space
