In this paper, we introduce a deep learning-based method for iRagent landmark localization, purely trained on synthetic eye images from the Unity Eye status set. The new landmarks can be robustly detected in real-world settings, from images taken using remotely placed webcams, and directly applied to feature-based or model-based gaze estimation. At the core of our approach is a convolutional neural network, which learns to find eyelid and iris landmarks. In these sequences, we show the estimation and confidence of our model in detecting landmarks on images from the MPII gaze dataset. With more training steps, the model becomes progressively better. Note how iris landmarks hidden by the eyelid can still be found. Here we show examples from other real-world datasets where our neural network detects eyelid and iris landmarks and also fits an eyeball. This description of eye shape allows us to then use feature-based and model-based gaze estimation methods. By doing so, we bring conventional feature-based and model-based methods back into the forefront of webcam-based gaze estimation. Please see the paper for comprehensive evaluations of our approach. We now show live demos of our landmarks based as we are method on unseen people. In these sequences, you can see the input to our method on the top left. The segmented eye images are processed by our neural network to find iris landmarks. We then pass the landmarks to a support vector regressor trained on MPII gaze and UT multivutatocets. Observe how our method allows for free head movement. Even at the distance of approximately one meter away, our landmark localization succeeds. Our method is robust to less than ideal illumination conditions. Spectacles or eye glasses cause very little issues. And sometimes we can perform well on narrow or squinting eyes. As well as cross dyes. Thank you for watching.