Summary of Ganfusion: Feed-forward Text-to-3d with Diffusion in Gan Space, by Souhaib Attaiki et al.
GANFusion: Feed-Forward Text-to-3D with Diffusion in GAN Space
by Souhaib Attaiki, Paul Guerrero, Duygu Ceylan, Niloy J. Mitra, Maks Ovsjanikov
First submitted to arxiv on: 21 Dec 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 research trains a feed-forward text-to-3D diffusion generator for human characters using only single-view 2D data as supervision. This novel approach addresses the limitations of existing 3D generative models, which struggle to match the fidelity of image or video generative models. The authors leverage the strengths of both GAN-based and diffusion-based generators by introducing GANFusion, a framework that combines unconditional triplane feature generation with text-conditioned diffusion modeling. This innovative approach enables the efficient training of high-quality 3D objects with text conditioning capabilities. The paper’s findings have implications for the development of 3D generative models in various applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’re working on creating machines that can generate 3D characters from just a single photo! Current attempts at this task are limited by the availability of training data and struggle to produce high-quality results. This research takes a different approach, combining two techniques to create a new way to generate 3D characters. The result is more realistic and flexible than previous methods. |
Keywords
» Artificial intelligence » Diffusion » Gan
