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+# Faze: Few-Shot Adaptive Gaze Estimation
+
+This repository will contain the code for training, evaluation, and live demonstration of our ICCV 2019 work, which was presented as an Oral presentation in Seoul, Korea. Faze is a framework for few-shot adaptation of gaze estimation networks, consisting of equivariance learning (via the **DT-ED** or Disentangling Transforming Encoder-Decoder architecture) and meta-learning with gaze embeddings as input.
+
+![The Faze Framework](https://ait.ethz.ch/projects/2019/faze/banner.jpg)
+
+## Setup
+Further setup instructions will be made available soon. For now, please pre-process the *GazeCapture* and *MPIIGaze* datasets using the code-base at https://github.com/swook/faze_preprocess
+
+## Additional Resources
+* Project Page (ETH Zurich): https://ait.ethz.ch/projects/2019/faze/
+* Project Page (Nvidia): https://research.nvidia.com/publication/2019-10_Few-Shot-Adaptive-Gaze
+* arXiv Page: https://arxiv.org/abs/1905.01941
+* Pre-processing Code: https://github.com/swook/faze_preprocess
+
+## Bibtex
+Please cite our paper when referencing or using our code.
+
+    @inproceedings{Park2019ICCV,
+      author    = {Seonwook Park and Shalini De Mello and Pavlo Molchanov and Umar Iqbal and Otmar Hilliges and Jan Kautz},
+      title     = {Few-Shot Adaptive Gaze Estimation},
+      year      = {2019},
+      booktitle = {International Conference on Computer Vision (ICCV)},
+      location  = {Seoul, Korea}
+    }
+
+## Acknowledgements
+
+Seonwook Park carried out this work during his internship at Nvidia. This work was supported in part by the ERC Grant OPTINT (StG-2016-717054).