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The previous approaches learned screen-space-textures for different features and a feature mask to compose them. Now it seems to actually learn the topology li
by Lichtso 5y ago
The previous approaches learned screen-space-textures for different features and a feature mask to compose them.
Now it seems to actually learn the topology lines of the human face [0], as 3D artists would learn them [1] when they study anatomy. It also uses quad grids and even places the edge loops and poles in similar places.
[0] https://nvlabs-fi-cdn.nvidia.com/_web/alias-free-gan/img/alias-free-gan-teaser-1920x1006.png https://nvlabs-fi-cdn.nvidia.com/_web/alias-free-gan/img/ali...
[1] https://i.pinimg.com/originals/6b/9a/0c/6b9a0c2d108b2be75bf72da8460cceb9.jpg https://i.pinimg.com/originals/6b/9a/0c/6b9a0c2d108b2be75bf7...
- eru 5y agoYes. It's interesting that imposing what are essentially 2d invariance constraints leads the network to learn what we regard as 3D concepts.
- pvillano 5y agoThere are some interesting 2d things our eyes do for 3d. If something is on the ground, half is above the horizon and half is below. Parallax is a 2d phenomenon.