Summary of What You See Is What You Gan: Rendering Every Pixel For High-fidelity Geometry in 3d Gans, by Alex Trevithick et al.
What You See is What You GAN: Rendering Every Pixel for High-Fidelity Geometry in 3D GANs
by Alex Trevithick, Matthew Chan, Towaki Takikawa, Umar Iqbal, Shalini De Mello, Manmohan Chandraker, Ravi Ramamoorthi, Koki Nagano
First submitted to arxiv on: 4 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); 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 techniques enable the training of 3D-aware Generative Adversarial Networks (GANs) to generate high-resolution 3D geometry with unprecedented detail. By leveraging learning-based samplers, neural volume rendering is scaled up to resolve fine-grained 3D geometry, allowing for explicit “rendering every pixel” during training and inference. This approach maintains image quality on par with baselines relying on post-processing super resolution, while synthesizing high-resolution 3D geometry and strictly view-consistent images. The method demonstrates state-of-the-art 3D geometric quality on FFHQ and AFHQ datasets, setting a new standard for unsupervised learning of 3D shapes in 3D GANs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a way to make computers better at creating detailed 3D images from 2D pictures. This is important because it could help us generate realistic characters or objects for movies and video games, or even create new 3D models of real-world objects. The researchers developed new techniques that allow the computer to “render” every pixel of a high-resolution image, rather than just looking at small parts of it like before. This helps the computer learn more about the shape and texture of the object, making the resulting 3D images look even more realistic. |
Keywords
* Artificial intelligence * Inference * Super resolution * Unsupervised
