Geoffrey Hinton: This is a really neat way of using neural networks to get rid of polygonal meshes. The interaction of computer graphics with neural networks is really exciting.
9 replies, 1004 likes
Andrea Tagliasacchi: NASA: Neural Articulated Shape Approximation
Representing characters and deformable shapes without polygonal meshes for 3D perception, games, ...
3 replies, 333 likes
Andrea Tagliasacchi: Double-blind is great, so why cannot #arXiv add an "anonymous till published" mode?
The authors could then be revealed only once the paper is full peer-reviewed (or after a cutoff date), and you could still cite them, thanks via https://arxiv.org/abs/1912.03207
1 replies, 59 likes
Shivon Zilis: Fitting that NASA outputs a spacesuit. Fascinating work!
0 replies, 16 likes
Nima Ghorbani: It is a wonderful, and humbling feeling to walk back home waiting to read two papers in your backpack during the holidays, which build on your previous work and go beyound what you could have achieved before! VIBE: https://arxiv.org/abs/1912.05656 and NASA: https://arxiv.org/abs/1912.03207
1 replies, 3 likes
Ryan Schmidt: Seems cool even though it doesn’t have any meshes.
0 replies, 3 likes
Daisuke Okanohara: NASA is the first neural implicit rig and can represent the shape of articulated objects. Each part is represented by an implicit function with a position and parameters being transformed by the pose. The whole is represented by their union (max op.). https://arxiv.org/abs/1912.03207
0 replies, 2 likes
Found on Apr 24 2020 at https://arxiv.org/pdf/1912.03207.pdf