TriangleGAN: A Python repository from yhlleo

TriangleGAN

A new gesture-to-gesture translation framework. Gesture-to-Gesture Translation in the Wild via Category-Independent Conditional Maps, published in ACM International Conference on Multimedia, 2019.

1.Dataset preparing

Original Dataset
- NTU Hand Gesture Dataset
- Senz3d Gesture Dataset

More details >>>

2.Installation

We provide an user-friendly configuring method via Conda system, and you can create a new Conda environment using the command:

conda env create -f environment.yml

3.Train/Test

1.Download dataset and copy them into ./datasets

2.Modify the scripts to train/test:

Training

sh ./scripts/train_trianglegan_ntu.sh <gpu_id>
sh ./scripts/train_trianglegan_senz3d.sh <gpu_id>

Testing

sh ./scripts/test_trianglegan_ntu.sh <gpu_id>
sh ./scripts/train_trianglegan_senz3d.sh <gpu_id>

3.The pretrained model is saved at ./checkpoints/{model_name}. Check here for all the available TriangleGAN models.

4.We provide an implementation of GestureGAN, ACM MM 2018 [paper]|[code].

sh ./scripts/train_gesturegan_ntu.sh <gpu_id>
sh ./scripts/train_gesturegan_senz3d.sh <gpu_id>

4.Evaluation

More Details >>>

5.Visual Results

More Details >>>

Acknowledgment

This code is based on the pytorch-CycleGAN-and-pix2pix. Thanks to the contributors of this project.

Related Work

Pose Guided Person Image Generation, NIPS 2017, [Tensorflow]
GANimation: Anatomically-aware Facial Animation from a Single Image, ECCV 2018, [PyTorch]
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation, CVPR 2018, [PyTorch]
GestureGAN for Hand Gesture-to-Gesture Translation in the Wild, ACM MM 2018, [Pytorch]
Animating Arbitrary Objects via Deep Motion Transfer, CVPR 2019, [Pytorch]

Evaluation codes

We recommend to evaluate the performances of the compared models mainly based on this repo: GAN-Metrics

References

If you take use of our datasets or code, please cite our papers:

@inproceedings{liu2019gesture,
  title={Gesture-to-gesture translation in the wild via category-independent conditional maps},
  author={Liu, Yahui and De Nadai, Marco and Zen, Gloria and Sebe, Nicu and Lepri, Bruno},
  booktitle={Proceedings of the 27th ACM International Conference on Multimedia},
  pages={1916--1924},
  year={2019}
}

If you have any questions, please contact me without hesitation (yahui.liu AT unitn.it).

yhlleo/TriangleGAN