Implementation for ECCV20 paper "Self-Supervised Learning of audio-visual objects from video"

Self-Supervised Learning of audio-visual objects from video.
Triantafyllos Afouras, Andrew Owens, Joon Son Chung, Andrew Zisserman
In ECCV 2020.

conda env create -f environment.yml
conda activate avobjects

Download pretrained model weights

bash download_models.bash

Run the separation demo

python main.py  --resume checkpoints/avobjects_loc_sep.pt --input_video demo.mp4 --output_dir demo_out 

The output directory will also contain videos with the separated audio for every tracked speaker.

Optional: You can point a web browser to the output directory to view the video results. If working on a remote machine, you can run a web server on port 8000 by running

cd demo_out; python3 -m http.server 8000

To run the model on a new video, add it into the media/ directory and select it using the --input_video argument.

You can specify the number of AV objects to track using the --n_peaks argument.

For example

python main.py  --resume checkpoints/avobjects_loc_sep.pt --n_peaks 2 --input_video trump_biden_debate.mp4  --output_dir trum_biden_debate_out   

If you use this code for your research, please cite:

@InProceedings{Afouras20b,
                 author       = "Triantafyllos Afouras and Andrew Owens and Joon~Son Chung and Andrew Zisserman",
                 title        = "Self-Supervised Learning of Audio-Visual Objects from Video",
                 booktitle    = "European Conference on Computer Vision",
                 year         = "2020",
                }