(Torch implementation) Fast artistic style transfer for videos, based on feedforward neural network
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README.md

Fast Artistic Style Transfer for Video

Torch implementation of a faster video stylization approach. See details and demos in project page.
demo
Watch demo videos on YouTube: Demo Video, Comparison With Other Methods

How to Install

#install torch
cd consistencyChecker
make
cd ..
bash download_models.sh

Example for Running

Below is an example to stylize video example.mp4 (Result resolution 640:480)

  • Put your video example.mp4 under this folder.
  • bash fast_stylize.sh *example.mp4* *models/starry_night.t7*
  • bash opt_flow.sh *example_01/frame_%06d.ppm* *example/flow_640:480*
  • bash make_video.sh *example.mp4*

*___* are parts you should change accordingly.
Please refer to this project if you want to train your own style model.

Deepmatching and deepflow are super slow. Run them parallelly on CPU cluster or run their GPU versions.
You only need optical flow to keep temporal consistency, not necessarily deepflow. We only tested with deepflow.

  • To run parallelly on k machines:
    • bash opt_flow.sh *example_01/frame_%06d.ppm* *example/flow_640:480* 1 k
    • bash opt_flow.sh *example_01/frame_%06d.ppm* *example/flow_640:480* 2 k
    • bash opt_flow.sh *example_01/frame_%06d.ppm* *example/flow_640:480* 3 k
    • ...
    • bash opt_flow.sh *example_01/frame_%06d.ppm* *example/flow_640:480* k-1 k
    • bash opt_flow.sh *example_01/frame_%06d.ppm* *example/flow_640:480* k k

Optical Flow

As suggested by #7 and #8, OF_DIS has been set to the default optical flow instead of deepflow to improve the efficiency. However, the pre-compiled run_OF_RGB was only tested on some Ubuntu systems, and if you have trouble running it, you might need to re-compile from source. Another option (also with slower speed but better quality) is to use the old deepflow. To use deepflow, you should change flowCommandLine to "bash run-deepflow.sh" in opt_flow.sh.

Why Faster

Use the real-time algorithm for stylization of each frame. So L-BFGS is only used for temporal consistency purpose. L-BFGS takes temporal loss, perceptual loss and relation loss into account and in addition, pixel loss to avoid contrast loss.

Reference

Implementation is based on the following projects:

Their license can be found in their project repository.

License

GNU GPL 3.0 for personal or research use. COMMERCIAL USE PROHIBITED.