Few-Shot Adaptive Gaze Estimation
It addresses the need for lower gaze errors in applications requiring higher quality, with incremental improvements in personalization using few calibration samples.
The paper tackles the problem of person-independent gaze estimation accuracy being limited by anatomical differences, proposing a few-shot adaptive framework (FAZE) that achieves state-of-the-art performance of 3.18 degrees on GazeCapture, a 19% improvement over prior art.
Inter-personal anatomical differences limit the accuracy of person-independent gaze estimation networks. Yet there is a need to lower gaze errors further to enable applications requiring higher quality. Further gains can be achieved by personalizing gaze networks, ideally with few calibration samples. However, over-parameterized neural networks are not amenable to learning from few examples as they can quickly over-fit. We embrace these challenges and propose a novel framework for Few-shot Adaptive GaZE Estimation (FAZE) for learning person-specific gaze networks with very few (less than or equal to 9) calibration samples. FAZE learns a rotation-aware latent representation of gaze via a disentangling encoder-decoder architecture along with a highly adaptable gaze estimator trained using meta-learning. It is capable of adapting to any new person to yield significant performance gains with as few as 3 samples, yielding state-of-the-art performance of 3.18 degrees on GazeCapture, a 19% improvement over prior art. We open-source our code at https://github.com/NVlabs/few_shot_gaze