| HuwCampbell/grenade |
1,430 |
|
3 |
0 |
over 2 years ago |
1 |
April 12, 2017 |
23 |
bsd-2-clause |
Haskell |
| Deep Learning in Haskell |
| XingangPan/GAN2Shape |
552 |
|
0 |
0 |
almost 3 years ago |
0 |
|
34 |
mit |
Python |
| Code for GAN2Shape (ICLR2021 oral) |
| junyanz/VON |
479 |
|
0 |
0 |
over 5 years ago |
0 |
|
8 |
other |
Python |
| [NeurIPS 2018] Visual Object Networks: Image Generation with Disentangled 3D Representation. |
| VITA-Group/ShapeMatchingGAN |
386 |
|
0 |
0 |
almost 4 years ago |
0 |
|
10 |
mit |
Jupyter Notebook |
| [ICCV 2019, Oral] Controllable Artistic Text Style Transfer via Shape-Matching GAN |
| czq142857/implicit-decoder |
186 |
|
0 |
0 |
almost 6 years ago |
0 |
|
0 |
other |
Python |
| The code for paper "Learning Implicit Fields for Generative Shape Modeling". |
| ChrisWu1997/PQ-NET |
99 |
|
0 |
0 |
over 3 years ago |
0 |
|
7 |
mit |
Python |
| code for our CVPR 2020 paper "PQ-NET: A Generative Part Seq2Seq Network for 3D Shapes" |
| robbiebarrat/Sculpture-GAN |
87 |
|
0 |
0 |
over 8 years ago |
0 |
|
2 |
|
Python |
| 3D-DCGAN trained on a corpus of 3D printable objects - as a result, the generations are usually 3D printable |
| marian42/shapegan |
85 |
|
0 |
0 |
almost 6 years ago |
0 |
|
1 |
|
Python |
| Generative Adversarial Networks and Autoencoders for 3D Shapes |
| zalandoresearch/disentangling_conditional_gans |
51 |
|
0 |
0 |
over 7 years ago |
0 |
|
4 |
mit |
Python |
| Disentangling Multiple Conditional Inputs in GANs |
| aarushgupta/FusionGAN |
13 |
|
0 |
0 |
over 7 years ago |
0 |
|
3 |
|
Jupyter Notebook |
| PyTorch Implementation of FusionGAN paper: Generating a Fusion Image: One's Identity and Another's Shape (https://arxiv.org/abs/1804.07455) |
