Deformable GANs for Pose-based Human Image Generation.

In orger to train a model:

Create folder market-dataset with 2 subfolder (test and train). Put the test images in test images in test/ and train images in train/.
Download pose estimator (conversion of this https://github.com/ZheC/Realtime_Multi-Person_Pose_Estimation) pose_estimator.h5. Launch python compute_cordinates.py. It will compute human keypoints.
Create pairs dataset with python create_pairs_dataset.py. It define pairs for training.
Run python train.py (see list of parameters in cmd.py)

Download checkpoints (https://yadi.sk/d/dxVvYxBw3QuUT9).
Run python test.py --generator_checkpoint path/to/generator/checkpoint (and same parameters as in train.py). It generate images and compute inception score, SSIM score and their masked versions.
To compute ssd_score. Download pretrained on VOC 300x300 model from https://github.com/weiliu89/caffe/tree/ssd. Put it in the ssd_score forlder. Run python compute_ssd_score.py --input_dir path/to/generated/images --img_index 2

Name		Name	Last commit message	Last commit date
Latest commit History 77 Commits
data		data
gan @ a4aa9a3		gan @ a4aa9a3
ssd_score		ssd_score
.gitmodules		.gitmodules
README.md		README.md
baseline.py		baseline.py
cmd.py		cmd.py
compute_coordinates.py		compute_coordinates.py
conditional_gan.py		conditional_gan.py
create_pairs_dataset.py		create_pairs_dataset.py
make_images_for_paper.py		make_images_for_paper.py
pose_dataset.py		pose_dataset.py
pose_transform.py		pose_transform.py
pose_utils.py		pose_utils.py
test.py		test.py
train.py		train.py

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