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Rendering Eyes for Eye-Tracking

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Eyes and their movements convey our attention and communicate social and emotional information. Recent large-scale supervised methods for eye-tracking require time-consuming data collection and manual annotation, which can be unreliable. In this talk I’ll describe how we instead used computer graphics to rapidly synthesize a varied, realistic, and cleanly-labelled dataset of eye-images from a collection of high-resolution head scans. I will then describe how we used the dataset to train an eye-region deformable model (CLNF), and a multimodal convolutional neural network for appearance-based gaze estimation. Finally I’ll discuss some future work for generating more varied synthetic datasets, and also getting eye-tracking to work robustly over large areas in-the-wild.

This talk is part of the Microsoft Research Cambridge, public talks series.

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