This repository contains an implementation of GANimation by Pumarola et al. based on StarGAN code by @yunjey. With this model they are able to modify in a continuous way facial expressions of single images.

Pretrained models and the preprocessed CelebA dataset are provided to facilitate the use of this model as well as the process for preparing other datasets for training this model.

Create your conda environment by just running the following command: conda env create -f environment.yml

Download and unzip the CelebA preprocessed dataset uploaded to this link extracted from MMLAB. Here you can find a folder containing the aligned and resized 128x128 images as well as a txt file containing their respective Action Units vectors computed using OpenFace. By default, this code assumes that you have these two elements in ./data/celeba/.

If you want to use other datasets you will need to detect and crop bounding boxes around the face of each image, compute their corresponding Action Unit vectors and resize them to 128x128px.

You can perform all these steps using OpenFace. First you will need to setup the project. They provide guides for linux and windows. Once the models are compiled, read their Action Unit wiki and their documentation on these models to find out which is the command that you need to execute.

In my case the command was the following: ./build/bin/FaceLandmarkImg -fdir datasets/my-dataset/ -out_dir processed/my-processed-dataset/ -aus -simalign -au_static -nobadaligned -simsize 128 -format_aligned jpg -nomask

After computing these Action Units, depending on the command that you have used, you will obtain different output formats. With the command that I used, I obtained a csv file for each image containing its corresponding Action Units vector among extra information, a folder for each image containing the resized and cropped image and a txt file with extra details about each image. You can find in openface_utils folder the code that I used to extract all the Action Unit information in a txt file and to group all the images into a single folder.

After having the Action Unit txt file and the image folder you can move them to the directory of this project. By default, this code assumes that you have these two elements in ./data/celeba/.

Pretrained models can be downloaded from this link. This folder contains the weights of both models (the Generator and the Discriminator) after training the model for 37 epochs.

By running python main.py --mode animation the default animation will be executed. There are two different types of animations already implemented which can be selected with the parameter 'animation_mode'. It is presuposed that the following folders are present:

• attribute_images: images from which the Action Units that we want to use for the animation were computed.
• images_to_animate: images that we want to animate.
• pretrained_models: pretrained models (only the generator is needed, you can download it from here
• results: folder where the resulting images will be stored.
• attributes.txt: file with the action units from 'attribute_images' computed.

The two options already implemented are the following:

• animate_image: applies the expressions from 'attributes.txt' to the images in 'images_to_animate'.
• animate_random_batch: applies the expressions from 'attributes.txt' to random batches of images from the training dataset.

You can either modify these parameters in main.py or by calling them as command line arguments.

• lambda_cls: classification lambda.
• lambda_rec: lambda for the cycle consistency loss.
• lambda_gp: gradient penalty lambda.
• lambda_sat: lambda for attention saturation loss.
• lambda_smooth: lambda for attention smoothing loss.

• c_dim: number of Action Units to use to train the model.
• batch_size
• num_epochs
• num_epochs_decay: number of epochs to start decaying the learning rate.
• g_lr: generator's learning rate.
• d_lr: discriminator's learning rate.

The weights are stored in the following format: <epoch>-<iteration>-<G/D>.ckpt where G and D represent the Generator and the Discriminator respectively. We save the state of thoptimizers in the same format and extension but add '_optim'.

• resume_iters: iteration numbre from which we want to start the training. Note that we will need to have a saved model corresponding to that exact iteration number.
• first_epoch: initial epoch for when we train from pretrained models.

• mode: train/test.
• image_dir: path to your image folder.
• attr_path: path to your attributes txt folder.
• outputs_dir: name for the output folder.

• use_virtual: this flag activates the use of cycle consistency loss during the training.

The aim of this new component is to minimize the noise produced by the Action Unit regression. This idea was extracted from Label-Noise Robust Multi-Domain Image-to-Image Translation by Kaneko et al.. It is not proven that this new component improves the outcomes of the model but the masks seem to be darker when it is applied without losing realism on the output images.

• Clean Test function. (DONE)
• Add an Action Units selector option for training.
• Add multi-gpu support.
• Smoother video generation.