/AutomEditor

AutomEditor is an AI based video editor that helps video bloggers to remove bloopers automatically. It uses multimodal spatio-temporal blooper recognition and localization approaches. The models were trained in keras and integrate feature fusion techniques from face, body gestures (skelethon), emotions progression, and audio features

Primary LanguagePythonMIT LicenseMIT

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AutomEditor

AutomEditor is an AI based automatic video editing tool that helps video bloggers to remove bloopers automatically. It uses multimodal spatio-temporal blooper recognition and localization approaches. The models were trained in keras and integrate feature fusion techniques from face, body gestures (skelethon), emotions progression, and audio features.

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Demo

http://www.carlostoxtli.com/AutomEditor/frontend/ (You need to link your own backend)

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Instructions

Requirements

Install the following software

  • ffmpeg
  • OpenSMILE
  • OpenFace
  • python 2 or 3 Place the OpenSMILE and OpenFace directories at the side of this cloned directory.

Data preprocessing

Run the scripts in the following order

  • ./backend/feature_extraction/extract_audio_files.py
  • ./backend/feature_extraction/generate_audio_feature.py
  • ./backend/feature_extraction/generate_face_feature.py
  • ./backend/feature_extraction/generate_face_visual.py
  • ./backend/feature_extraction/generate_emotion_feature.py
  • ./backend/feature_extraction/generate_body_feature.py
  • ./backend/feature_extraction/generate_body_visual.py Copy all the .pkl files to the ./backend/data/ folder

Training

For training individual models just give the specific model

  • ./backend/experiment/train.py --model emotion_feature For training fusion models just give the comma separated list of models and add the fusion parameter
  • ./backend/experiment/train.py --fusion --model body_fusion,face_fusion,audio_feature,emotion_feature

User interface

Run the web server

  • ./backend/feature_extraction/server.py Expose the frontend directory to the web, for instance:
  • cd ./frontend/
  • sudo python -m SimpleSTTPServer 80 Navigate the URL, for instance: http://localhost

Architecture

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References

This work is based on this paper: https://arxiv.org/pdf/1805.00625.pdf

Slides: https://docs.google.com/presentation/d/1ds8nGurfxUgnizWwI61eLh8OTJ4l4K486LwPe38I0Eo

Publication in progress ...