Despite the recent development of learning-based gaze estimation methods,
most methods require one or more eye or face region crops as inputs and produce
a gaze direction vector as output. Cropping results in a higher resolution in
the eye regions and having fewer confounding factors (such as clothing and
hair) is believed to benefit the final model performance. However, this
eye/face patch cropping process is expensive, erroneous, and
implementation-specific for different methods. In this paper, we propose a
frame-to-gaze network that directly predicts both 3D gaze origin and 3D gaze
direction from the raw frame out of the camera without any face or eye
cropping. Our method demonstrates that direct gaze regression from the raw
downscaled frame, from FHD/HD to VGA/HVGA resolution, is possible despite the
challenges of having very few pixels in the eye region. The proposed method
achieves comparable results to state-of-the-art methods in Point-of-Gaze (PoG)
estimation on three public gaze datasets: GazeCapture, MPIIFaceGaze, and EVE,
and generalizes well to extreme camera view changes.

该论文提出了一种基于帧的直接预测 3D 注视原点和 3D 注视方向的网络，在三个公共焦点数据集上实现了可比较的结果。

EFE: 端到端的帧到凝视估计

EFE: End-to-end Frame-to-Gaze Estimation

As an indicator of human attention gaze is a subtle behavioral cue which can
be exploited in many applications. However, inferring 3D gaze direction is
challenging even for deep neural networks given the lack of large amount of
data (groundtruthing gaze is expensive and existing datasets use different
setups) and the inherent presence of gaze biases due to person-specific
difference. In this work, we address the problem of person-specific gaze model
adaptation from only a few reference training samples. The main and novel idea
is to improve gaze adaptation by generating additional training samples through
the synthesis of gaze-redirected eye images from existing reference samples. In
doing so, our contributions are threefold: (i) we design our gaze redirection
framework from synthetic data, allowing us to benefit from aligned training
sample pairs to predict accurate inverse mapping fields; (ii) we proposed a
self-supervised approach for domain adaptation; (iii) we exploit the gaze
redirection to improve the performance of person-specific gaze estimation.
Extensive experiments on two public datasets demonstrate the validity of our
gaze retargeting and gaze estimation framework.

本论文主要创新点是通过合成现有的参考样本的注视重定向眼图像来改进视线适应，并提出了一个自监督的方法进行域适应，利用视线重定向来提高特定个体视线估计的性能，通过对两个公共数据集的广泛实验证明了我们的视线重新定向和视线估计框架的有效性。