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Source Code for the paper "Unsupervised Domain Adaptation for Learning Eye Gaze from a Million Synthetic Images: An Adversarial Approach"

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Source Code for the paper "Unsupervised Domain Adaptation for Learning Eye Gaze from a Million Synthetic Images: An Adversarial Approach"

Data Generation

Use the scripts in the folder eye_gaze/dataio for processing data and generating TFRecords

Synthetic Images

  1. Generate the synthetic images from the UnityEyes simulator
  2. Run python gaze_cropper.py with applicable paths to process the raw images
  3. Run python gaze_converter.py with applicable paths for generating TFRecords from processed images

Real Images

  1. Download MPIIGaze dataset into a folder.
  2. Run python real_gaze_converter.py with applicable path to generator TFRecords for real images

Training Steps

Use the scripts in the folder eye_gaze/src for training and evaluating the models.

For training source model [pretraining the generator]

  1. Run python src/gaze_train_regressor.py from eye_gaze folder, with the input arguments set likewise.
  2. gaze_train_regressor.py essentially calls gaze_model_regressor with the supplied arguments and starts the training process

For training target model [generator] and discriminator

  1. Run python src/gaze_da_train_regressor.py from eye_gaze folder, with the input arguments set likewise.
  2. gaze_da_train_regressor.py essentially calls gaze_da_model_regressor with the supplied arguments and starts the training process

Evaluating the model

  1. Run python src/gaze_train_regressor.py with evaluate set to True, checkpoint_dir and checkpoint_file to point to the trained model

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Source Code for the paper "Unsupervised Domain Adaptation for Learning Eye Gaze from a Million Synthetic Images: An Adversarial Approach"

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