https://github.com/somewacko/deconvfaces

Generating faces with deconvolution networks
https://github.com/somewacko/deconvfaces

animation deep-learning keras

Last synced: 2 months ago
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Generating faces with deconvolution networks

• Host: GitHub
• URL: https://github.com/somewacko/deconvfaces
• Owner: somewacko
• License: mit
• Created: 2016-04-27T03:11:22.000Z (about 9 years ago)
• Default Branch: master
• Last Pushed: 2021-06-08T20:08:11.000Z (almost 4 years ago)
• Last Synced: 2024-07-31T22:50:45.849Z (10 months ago)
• Topics: animation, deep-learning, keras
• Language: Python
• Size: 1.75 MB
• Stars: 893
• Watchers: 36
• Forks: 130
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Generating Faces with Deconvolution Networks

![Example generations](img/example.gif)

This repo contains code to train and interface with a deconvolution network adapted from [this paper][Chairs] to generate faces using data from the [Radboud Faces Database][RaFD]. Requires [Keras][Keras], [NumPy][NumPy], [SciPy][SciPy], and [tqdm][tqdm] with Python 3 to use.

## Training New Models

To train a new model, simply run:

    python3 faces.py train path/to/data

    

You can specify the number of deconvolution layers with `-d` to generate larger images, assuming your GPU has the memory for it. You can play with the batch size and the number of kernels per layer (using `-b` and `-k` respectively) until it fits in memory, although this may result in worse results or longer training.

Using 6 deconvolution layers with a batch size of 8 and the default number of kernels per layer, a model was trained on an Nvidia Titan X card (12 GB) to generate 512x640 images in a little over a day.

## Generating Images

To generate images using a trained model, you can specify parameters in a yaml file and run:

    python3 faces.py generate -m path/to/model -o output/directory -f path/to/params.yaml

There are four different modes you can use to generate images:

* `single`, produce a single image.

* `random`, produce a set of random images.

* `drunk`, similar to random, but produces a more contiguous sequence of images.

* `interpolate`, animate between a set of specified keyframes.

You can find examples of these files in the `params` directory, which should give you a good idea of how to format these and what's available.

## Examples

Interpolating between identities and emotions:

[![Interpolating between identities and emotions](http://img.youtube.com/vi/UdTq_Q-WgTs/0.jpg)](https://www.youtube.com/watch?v=UdTq_Q-WgTs)

Interpolating between orientations: (which the model is unable to learn)

[![Interpolating between orientation](http://img.youtube.com/vi/F4OFkN3EURk/0.jpg)](https://www.youtube.com/watch?v=F4OFkN3EURk)

Random generations (using "drunk" mode):

[![Random generations](http://img.youtube.com/vi/vt8zNvJNjSo/0.jpg)](https://www.youtube.com/watch?v=vt8zNvJNjSo)

[Chairs]: https://arxiv.org/abs/1411.5928

[RaFD]: http://www.socsci.ru.nl:8180/RaFD2/RaFD?p=main

[Keras]: https://keras.io/

[NumPy]: http://www.numpy.org/

[SciPy]: https://www.scipy.org/

[tqdm]: https://github.com/noamraph/tqdm