Efficient Geometry-Aware 3D Generative Adversarial Networks - EG3D

Explore a hands-on, step-by-step guide for using pre-built Efficient Geometry-aware 3D Generative Adversarial Networks (EG3D) models on an Ubuntu 22.04 Linux machine with Python 3.9, PyTorch, and various CuDA GPU deep learning libraries. Learn to set up the environment, download models, load visualizers, and work with AFHQ Cat and Sports Car models. Discover techniques for exporting videos, generating 3D shapes, and loading them in ChimeraX. Create 3D shape and volume movies, and access valuable GitHub resources for further exploration. This comprehensive tutorial covers everything from initial setup to advanced visualization techniques in just 21 minutes.

- Video Start
- Content Intro
- Setting Environment for EG3D
- Downloading Model
- Loading Model Visualizer
- Visualizing AFHQ Cat Model
- Visualizing Sports Car Model
- Exporting Video from Models
- Generating 3D Shapes
- Loading 3D Shapes in ChimeraX
- 3D Shape and Volume Movies
- GitHub Resources