Automatic Seamless Face Replacement in Videos

-Yiren Lu (luyiren [at] seas [dot] upenn [dot] edu)
-Dongni Wang (wdongni [at] seas [dot] upenn [dot] edu)

Automatic Seamless Face Replacement (without deep learning).

Project Description

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Run Code

Python third party libs required:

• dlib
• cv2
• skimage
• scipy.io

To run face replacement:

• Download face landmark estimation model and uncompress

$ wget http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
$ tar xvfj shape_predictor_68_face_landmarks.dat.bz2

• Face detection (for both source video and destination video)

$ Python face_detect_wrapper.py shape_predictor_68_face_landmarks.dat [video_name_no_suffix]

e.g.:

$ Python face_detect_wrapper.py shape_predictor_68_face_landmarks.dat clips/clip1

Example outputs in Proj4_Test/ and clips/

• Face replacement: see demo.m
  • load face detection results output by 1.
  • run replace_all_faces([src video path], [replacement video path], [src video detection results], [source video detection results], [destination video detection results], [source face index], [resize x])
  • save video to .avi file

Example output videos in output_videos/

Video Demo

• Test videos could be downloaded here:
  https://drive.google.com/drive/folders/0Bw-gLvstCiC-b1IxSE4xemo2eUk?usp=sharing

License

The contents of this repository are licensed under the MIT License.