Summary of Gasp: Gaussian Avatars with Synthetic Priors, by Jack Saunders et al.
GASP: Gaussian Avatars with Synthetic Priors
by Jack Saunders, Charlie Hewitt, Yanan Jian, Marek Kowalski, Tadas Baltrusaitis, Yiye Chen, Darren Cosker, Virginia Estellers, Nicholas Gyde, Vinay P. Namboodiri, Benjamin E Lundell
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
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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 Gaussian Splatting has revolutionized real-time photo-realistic rendering, particularly in the creation of animatable avatars known as Gaussian Avatars. Recent advancements have pushed the boundaries of quality and rendering efficiency but are limited by either requiring expensive multi-camera rigs or being trained with a single camera, restricting high-quality rendering to fixed viewpoints. To overcome these limitations, we propose GASP: Gaussian Avatars with Synthetic Priors, which leverages the pixel-perfect nature of synthetic data to train a prior model. By fitting this prior to a single photo or video and fine-tuning it, we obtain high-quality Gaussian Avatars that support 360° rendering. Our method enables real-time application, achieving 70fps rendering on commercial hardware. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GASP is a new way to create really realistic avatars that can move around in videos. Right now, making these avatars is hard because it requires special equipment or only works from one fixed view. GASP fixes this problem by using fake data (synthetic) to train the model and then fine-tuning it with real data. This allows us to create high-quality avatars that can be animated and rendered in real-time, making them perfect for things like video games. |
Keywords
» Artificial intelligence » Fine tuning » Synthetic data
