Your hands-on guide to Efficient Geometry-aware 3D Generative Adversarial Networks (EG3D)
Prodramp
This video is your hands on step-by-step guide to use pre-built EG3D-Efficient Geometry-aware 3D Generative Adversarial Networks models of various kind on your Ubuntu 22.04 Linux machine with Python 3.9, PyTorch and various CuDA GPU deep learning libraries.
GitHub Resources: https://github.com/prodramp/DeepWorks/tree/main/EG3D https://github.com/NVlabs/eg3d
ā¬ā¬ā¬ā¬ā¬ā¬ ā° TUTORIAL TIME STAMPS ā° ā¬ā¬ā¬ā¬ā¬ā¬
- (00:00) Video Start
- (01:10) Content Intro
- (03:15) Setting Environment for EG3D
- (06:00) Downloading Model
- (07:00) Loading Model Visualizer
- (09:30) Visualizing AFHQ Cat Model
- (15:05) Visualizing Sports Car Model
- (15:40) Exporting Video from Models
- (17:00) Generating 3D Shapes
- (17:35) Loading 3D Shapes in ChimeraX
- (18:45) 3D Shape and Volume Movies
- (20:15) GitHub Resources
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Content Creator: Avkash Chauhan (@avkashchauhan)
Tags: #EG3D #nerf #nvidia #ai #ml #lime #aicloud #h2oai #driverlessai #machinelearning #cloud #mlops #model #collaboration #deeplearning #modelserving #modeldeployment #pytorch #datarobot #datahub #streamlit #modeltesting #codeartifact #dataartifact #modelartifact #onnx #aws #kaggle #mapbox #lightgbm #xgboost #classification #dataengineering #pandas #keras #tensorflow #tensorboard #cnn #prodramp #avkashchauhan #LIME #mli #blender #meshlab ... https://www.youtube.com/watch?v=7tG37T6bsBw
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