Summary of Im-3d: Iterative Multiview Diffusion and Reconstruction For High-quality 3d Generation, by Luke Melas-kyriazi et al.
IM-3D: Iterative Multiview Diffusion and Reconstruction for High-Quality 3D Generation
by Luke Melas-Kyriazi, Iro Laina, Christian Rupprecht, Natalia Neverova, Andrea Vedaldi, Oran Gafni, Filippos Kokkinos
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper presents an innovative approach to text-to-3D generation by exploring the design space of text-to-3D models. The authors improve multi-view generation by using video generators instead of image-based ones, which significantly enhances the quality and efficiency of the process. The new method, IM-3D, combines a 2D generator with a 3D reconstruction algorithm that utilizes Gaussian splatting to optimize an image-based loss function. This results in direct production of high-quality 3D outputs from generated views, reducing the computational cost by 10-100x and increasing the yield of usable 3D assets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you want to create 3D objects from text descriptions. Most people use pre-trained models that can convert text into images, but these models are slow and not very good at creating 3D objects. The authors of this paper have a new idea: instead of using images, why not use videos? This helps create more realistic 3D objects and makes the process faster and better. |
Keywords
* Artificial intelligence * Loss function
