cnc-iiser-bhopal / any-body-can-dance

EverybodyDanceNow reproduced in pytorch

Written by Aman Arya under supervision of Parthiban Srinivasan .

We train and evaluate on windows 10 in syder 4.1

About:

Project Title : Any Body Can Dance

Youtube link : https://www.youtube.com/watch?v=LWEm9bDlVmY&pbjreload=101

Faculty supervisor: Parthiban Srinivasan ( Adjunct Faculty (Artificial Intelligence) at IISER, Bhopal and CEO, Parthys Reverse Informatics )

https://in.linkedin.com/in/parthiban-srinivasan-183608b

Github code: https://github.com/aman-arya/EverybodyDanceNow_reproduce_pytorch

Research paper : https://arxiv.org/pdf/1808.07371.pdf

Presentation : https://drive.google.com/file/d/1suHPXofvexaztPkBwDECmwHiTjiOQhq5/view?usp=sharing

Reference:

https://arxiv.org/pdf/1808.07371.pdf

https://github.com/CUHKSZ-TQL/EverybodyDanceNow_reproduce_pytorch

https://carolineec.github.io/everybody_dance_now/

https://getsway.app/

https://people.eecs.berkeley.edu/~efros/

Pre-trained models and source video

• Download vgg19-dcbb9e9d.pth.crdownload here and put it in ./src/pix2pixHD/models/
  

• Download pose_model.pth here and put it in ./src/PoseEstimation/network/weight/
  

• Source video can be download from here

• Download pre-trained vgg_16 for face enhancement here and put in ./face_enhancer/

Full process

Pose2vid network

Make source pictures

• Put source video mv.mp4 in ./data/source/
• Run make_source.py, the label images and coordinate of head will save in ./data/source/test_label_ori/ and ./data/source/pose_souce.npy (will use in step6).
• If you want to capture video by camera, you can directly run ./src/utils/save_img.py

Make target pictures

• Put target video mv.mp4 in ./data/target/
• Run make_target.py, pose.npy will save in ./data/target/, which contain the coordinate of faces (will use in step6).

Train and use pose2vid network

• Run train_pose2vid.py and check loss and full training process in ./checkpoints/

• If you break the traning and want to continue last training, set load_pretrain = './checkpoints/target/ in ./src/config/train_opt.py

• Run normalization.py rescale the label images, you can use two sample images from ./data/target/train/train_label/ and ./data/source/test_label_ori/ to complete normalization between two skeleton size

• Run transfer.py and get results in ./result

Face enhancement network

Train and use face enhancement network

• Run ./face_enhancer/prepare.py and check the results in ./data/face/test_sync and ./data/face/test_real.
• Run ./face_enhancer/main.py train face enhancer and run./face_enhancer/enhance.py to gain results
  This is comparision in original (left), generated image before face enhancement (median) and after enhancement (right). FaceGAN can learn the residual error between the real picture and the generated picture faces.

Performance of face enhancement

Gain results

• Run make_gif.py and make result pictures to gif picture

TODO

• Pose estimation
  • Pose
  • Face
  • Hand
• pix2pixHD
• FaceGAN
• Temporal smoothing

Environments

windows 10
Python 3.7
Pytorch 1.0
OpenCV 3.4.2.17