Gaze Estimation
83 papers with code • 11 benchmarks • 17 datasets
Gaze Estimation is a task to predict where a person is looking at given the person’s full face. The task contains two directions: 3-D gaze vector and 2-D gaze position estimation. 3-D gaze vector estimation is to predict the gaze vector, which is usually used in the automotive safety. 2-D gaze position estimation is to predict the horizontal and vertical coordinates on a 2-D screen, which allows utilizing gaze point to control a cursor for human-machine interaction.
Source: A Generalized and Robust Method Towards Practical Gaze Estimation on Smart Phone
Most implemented papers
Fine-Grained Head Pose Estimation Without Keypoints
Estimating the head pose of a person is a crucial problem that has a large amount of applications such as aiding in gaze estimation, modeling attention, fitting 3D models to video and performing face alignment.
Learning from Simulated and Unsupervised Images through Adversarial Training
With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations.
MobileOne: An Improved One millisecond Mobile Backbone
Furthermore, we show that our model generalizes to multiple tasks - image classification, object detection, and semantic segmentation with significant improvements in latency and accuracy as compared to existing efficient architectures when deployed on a mobile device.
Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark
This paper serves not only as a reference to develop deep learning-based gaze estimation methods, but also a guideline for future gaze estimation research.
Appearance-Based Gaze Estimation in the Wild
Appearance-based gaze estimation is believed to work well in real-world settings, but existing datasets have been collected under controlled laboratory conditions and methods have been not evaluated across multiple datasets.
MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation
Second, we present an extensive evaluation of state-of-the-art gaze estimation methods on three current datasets, including MPIIGaze.
It's Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation
Eye gaze is an important non-verbal cue for human affect analysis.
Recurrent CNN for 3D Gaze Estimation using Appearance and Shape Cues
Gaze behavior is an important non-verbal cue in social signal processing and human-computer interaction.
3DGazeNet: Generalizing Gaze Estimation with Weak-Supervision from Synthetic Views
To close the gap between image domains, we create a large-scale dataset of diverse faces with gaze pseudo-annotations, which we extract based on the 3D geometry of the scene, and design a multi-view supervision framework to balance their effect during training.
Eye Tracking for Everyone
We believe that we can put the power of eye tracking in everyone's palm by building eye tracking software that works on commodity hardware such as mobile phones and tablets, without the need for additional sensors or devices.