UVAGaze: Unsupervised 1-to-2 Views Adaptation for Gaze Estimation
Ruicong Liu Feng Lu
Gaze estimation has become a subject of growing interest in recent research.
Most of the current methods rely on single-view facial images as input. Yet, it
is hard for these approaches to handle large head angles, leading to potential
inaccuracies in the estimation. To address this issue, adding a second-view
camera can help better capture eye appearance. However, existing multi-view
methods have two limitations. 1) They require multi-view annotations for
training, which are expensive. 2) More importantly, during testing, the exact
positions of the multiple cameras must be known and match those used in
training, which limits the application scenario. To address these challenges,
we propose a novel 1-view-to-2-views (1-to-2 views) adaptation solution in this
paper, the Unsupervised 1-to-2 Views Adaptation framework for Gaze estimation
(UVAGaze). Our method adapts a traditional single-view gaze estimator for
flexibly placed dual cameras. Here, the "flexibly" means we place the dual
cameras in arbitrary places regardless of the training data, without knowing
their extrinsic parameters. Specifically, the UVAGaze builds a dual-view mutual
supervision adaptation strategy, which takes advantage of the intrinsic
consistency of gaze directions between both views. In this way, our method can
not only benefit from common single-view pre-training, but also achieve more
advanced dual-view gaze estimation. The experimental results show that a
single-view estimator, when adapted for dual views, can achieve much higher
accuracy, especially in cross-dataset settings, with a substantial improvement
of 47.0%. Project page: https://github.com/MickeyLLG/UVAGaze.