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Efficient Geometry-aware 3D Generative Adversarial Networks | GAN Paper Explained

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In this video I cover the “Efficient Geometry-aware 3D Generative Adversarial Networks” paper, that introduced a novel explicit-implicit 3D scene representation network and devised a novel framework (dual discrimination, gen/disc pose conditioning, etc.) for SOTA 3D GANs.

I also explain how NeRF works in case you didn’t have to chance to read it, yet.

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✅ This video paper: https://arxiv.org/abs/2112.07945
✅ Website: https://matthew-a-chan.github.io/EG3D/

Supporting papers:
✅ NeRF paper: https://arxiv.org/abs/2003.08934
✅ Explicit voxel grids paper: https://arxiv.org/abs/1906.07751
✅ StyleGAN v1: https://arxiv.org/abs/1812.04948
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⌚️ Timetable:
00:00 Intro
02:45 Tri-plane 3D scene representation intro
04:25 NeRF in depth
13:35 Explicit voxel grid methods
14:40 Tri-plane 3D scene representation explained
16:40 Tri-plane is as expressive
18:30 Efficient 3D GANs pipeline
22:05 Pose correlated facial features
23:40 Dual discrimination, super-resolution
32:40 Results
37:00 Ethical considerations, are we going to change anything?
39:25 Intrinsics, extrinsics robustness?
42:00 Outro

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#3D #GANs #NeRF