Edited from VITON Readme:

Zalando dataset: Google Drive.

Put all folder and labels in the data folder:

data/women_top: reference images (image name is ID_0.jpg) and clothing images (image name is ID_1.jpg). For example, the clothing image on reference image 000001_0.jpg is 000001_1.jpg. The resolution of these images is 1100x762.

data/pose.pkl: a pickle file containing a dictionary of the pose keypoints of each reference image. We used https://github.com/ZheC/Realtime_Multi-Person_Pose_Estimation to extract Pose keypoints.

NOTE : Use the demo.py from this REPO to save pose keypoints as pickle file. instead of original demo.ipynb

data/segment: folder containing the segmentation map of each reference image. Used LIPP_JPPNET for that.

Test

First stage

Download pretrained models on Google Drive. Put them under model/ folder.

Run test_stage1.sh to do the inference. The results are in results/stage1/images/. results/stage1/index.html visualizes the results.

Second stage

Run the matlab script shape_context_warp.m to extract the TPS transformation control points.

Then test_stage2.sh will do the refinement and generate the final results, which locates in results/stage2/images/. results/stage2/index.html visualizes the results.

Train

Prepare data

Go inside prepare_data.

First run extract_tps.m. This will take sometime, you can try run it in parallel or directly download the pre-computed TPS control points via Google Drive and put them in data/tps/.

Then run ./preprocess_viton.sh, and the generated TF records will be in prepare_data/tfrecord.

First stage

Run train_stage1.sh

Second stage

Run train_stage2.sh