Generate images from human pose
python 3.6
tensorflow 2.3
numpy
matplotlib
The preparation stage simply splits the dataset into train, validation, and test sets. Since the train and test sets are came from the same origin, no data agumentation is applied for now. But, to generalize the model, we should consider to use random crop, horizontal flip, or small rotation.
Run
python preprocess.py
to split the dataset into 80/20/20. All paths are configured at config.py.
To train a model, run
python train.py
It will store the model at saved_model.
To test the model, run
python test.py
This will generate images using the test set defined above. As a default, it will process every 4 images (batch size) and store the images at the imgs directory.
The model weights can be downloaded from Google Drive. Store the model weight at the saved_model directory.
The structural similarity index measure (SSIM) index is used to measure the similarity between the target and generated images.
The average of SSIM indices at the test set is 0.8366.
Note that the label test indicates the validation set, not the test set.
