All Projects → Yang7879 → 3d Recgan

Yang7879 / 3d Recgan

Licence: mit
🔥3D-RecGAN in Tensorflow (ICCV Workshops 2017)

Programming Languages

python
139335 projects - #7 most used programming language

Projects that are alternatives of or similar to 3d Recgan

3d Recgan Extended
🔥3D-RecGAN++ in Tensorflow (TPAMI 2018)
Stars: ✭ 98 (-15.52%)
Mutual labels:  3d-reconstruction, generative-adversarial-network, gans
Von
[NeurIPS 2018] Visual Object Networks: Image Generation with Disentangled 3D Representation.
Stars: ✭ 497 (+328.45%)
Mutual labels:  generative-adversarial-network, gans
Fast Srgan
A Fast Deep Learning Model to Upsample Low Resolution Videos to High Resolution at 30fps
Stars: ✭ 417 (+259.48%)
Mutual labels:  generative-adversarial-network, gans
Contrastive Unpaired Translation
Contrastive unpaired image-to-image translation, faster and lighter training than cyclegan (ECCV 2020, in PyTorch)
Stars: ✭ 822 (+608.62%)
Mutual labels:  generative-adversarial-network, gans
Sdv
Synthetic Data Generation for tabular, relational and time series data.
Stars: ✭ 360 (+210.34%)
Mutual labels:  generative-adversarial-network, gans
Selectiongan
[CVPR 2019 Oral] Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation
Stars: ✭ 366 (+215.52%)
Mutual labels:  generative-adversarial-network, gans
Gans In Action
Companion repository to GANs in Action: Deep learning with Generative Adversarial Networks
Stars: ✭ 748 (+544.83%)
Mutual labels:  generative-adversarial-network, gans
Deep-Learning
It contains the coursework and the practice I have done while learning Deep Learning.🚀 👨‍💻💥 🚩🌈
Stars: ✭ 21 (-81.9%)
Mutual labels:  generative-adversarial-network, gans
Co Mod Gan
[ICLR 2021, Spotlight] Large Scale Image Completion via Co-Modulated Generative Adversarial Networks
Stars: ✭ 46 (-60.34%)
Mutual labels:  generative-adversarial-network, gans
Pacgan
[NeurIPS 2018] [JSAIT] PacGAN: The power of two samples in generative adversarial networks
Stars: ✭ 67 (-42.24%)
Mutual labels:  generative-adversarial-network, gans
Bicyclegan
Toward Multimodal Image-to-Image Translation
Stars: ✭ 1,215 (+947.41%)
Mutual labels:  generative-adversarial-network, gans
Faceswap Gan
A denoising autoencoder + adversarial losses and attention mechanisms for face swapping.
Stars: ✭ 3,099 (+2571.55%)
Mutual labels:  generative-adversarial-network, gans
Generative models tutorial with demo
Generative Models Tutorial with Demo: Bayesian Classifier Sampling, Variational Auto Encoder (VAE), Generative Adversial Networks (GANs), Popular GANs Architectures, Auto-Regressive Models, Important Generative Model Papers, Courses, etc..
Stars: ✭ 276 (+137.93%)
Mutual labels:  generative-adversarial-network, gans
Anycost Gan
[CVPR 2021] Anycost GANs for Interactive Image Synthesis and Editing
Stars: ✭ 367 (+216.38%)
Mutual labels:  generative-adversarial-network, gans
DLSS
Deep Learning Super Sampling with Deep Convolutional Generative Adversarial Networks.
Stars: ✭ 88 (-24.14%)
Mutual labels:  generative-adversarial-network, gans
Data Efficient Gans
[NeurIPS 2020] Differentiable Augmentation for Data-Efficient GAN Training
Stars: ✭ 682 (+487.93%)
Mutual labels:  generative-adversarial-network, gans
gans-collection.torch
Torch implementation of various types of GAN (e.g. DCGAN, ALI, Context-encoder, DiscoGAN, CycleGAN, EBGAN, LSGAN)
Stars: ✭ 53 (-54.31%)
Mutual labels:  generative-adversarial-network, gans
skip-thought-gan
Generating Text through Adversarial Training(GAN) using Skip-Thought Vectors
Stars: ✭ 44 (-62.07%)
Mutual labels:  generative-adversarial-network, gans
Bringing Old Photos Back To Life
Bringing Old Photo Back to Life (CVPR 2020 oral)
Stars: ✭ 9,525 (+8111.21%)
Mutual labels:  generative-adversarial-network, gans
Doppelganger
[IMC 2020 (Best Paper Finalist)] Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions
Stars: ✭ 97 (-16.38%)
Mutual labels:  generative-adversarial-network, gans

3D Object Reconstruction from a Single Depth View with Adversarial Learning

Bo Yang, Hongkai Wen, Sen Wang, Ronald Clark, Andrew Markham, Niki Trigoni. In ICCV Workshops, 2017.

Teaser_Image

Paper

https://arxiv.org/abs/1708.07969

Data

https://drive.google.com/open?id=1n4qQzSd_S6Isd6WjKD_sq6LKqn4tiQm9

Data are also available at Baidu Pan:

https://pan.baidu.com/s/165IXaA_JISCwGzTUCiuPig 提取码: gbp2

Requirements

python 2.7

tensorflow 1.1.0

numpy 1.12.1

scipy 0.19.0

Run

python main_3D-RecGAN.py

Citation

If you use the paper, code or data for your research, please cite:

@inProceedings{Yang17,
  title={3D Object Reconstruction from a Single Depth View with Adversarial Learning},
  author = {Bo Yang
  and Hongkai Wen
  and Sen Wang
  and Ronald Clark
  and Andrew Markham
  and Niki Trigoni},
  booktitle={International Conference on Computer Vision Workshops (ICCVW)},
  year={2017}
}
Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].