I recorded the video on my own. If you have few data make sure - video background is as simple as possible, no camera vibration during recording, uniform intensity of light in video .
you can also use this video. This is an ideal one
Afrobeats Dance Workout - 20 Minutes Fat Burning Workout
Ubuntu 16/18. (I used 18.04)
python 3.6.4
opencv: 3.4
pip install opencv-contrib-python==3.4.4.19Pytorch 0.4.1
Torchvision 0.2.1
conda install pytorch=0.4.1 torchvision cuda90 -c pytorch(this command will install both pytorch and torchvision)
dominate
pip install dominate tqdm
pip install tqdm 
step-1: keep video of a person making free movements in 'data/target/video' folder
step-2: Extract Image from video. run '1.pre_processing.py'. It extracts images from video. You can control rate of extraction using frame_interval. Cropping and rotating function are also written. Output of this stage is 512 x 512 pixels images
step-3: Pose Estimation. I used OpenPose for pose estimation. run '2.image_to_stick_figure.py' to get stick diagram of folder 'data/target/images'. Stick figures are saved in 'data/train/train_label' folder.
step-4: Pix2pixHD training. run '3.train_pose2vid.py' to learn the mapping of stick figure to realistic image. The generator and discriminator trained model will be saved 'checkpoints/target' folder.
step-5: Give input stick diagrams to 'data/source' folder
step-6: run '4.transfer.py'. It will translate stick diagrams of 'data/source' folder to realistic image in 'result/target/test_latest/images' folder
step-7: run '5.images_to_video.py' to create video of result images.