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 ...
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 ...
[Cheng-etal2021 arXiv] Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark [PDF] Yihua Cheng, Haofei Wang, Yiwei Bao, Feng Lu[Kar-Corcoran2017 IEEE] A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms...
We used 1-channel bitmap for enhancing gaze estimation accuracy, but like other papers which use 3-channel RGB images as input, we provide 3-channel image mode. You can change the mode with THREE-CHANNEL flag. We also provide various options for you to test your model with various model ...
CNN-based gaze estimation methods are usually trained in an end-to-end manner with substantial amount of la- beled data. The performance of CNN-based gaze estimation methods highly relies on the quantity and diversity of the training dataset. Unfortunately, annotating g...
We open-source our code at https: //github.com/NVlabs/few_shot_gaze 1. 1. Introduction Estimation of human gaze has numerous applications in human-computer interaction [7], virtual reality [28], auto- motive [41] and content creation [46], among others. Many of these applications require...
The above survey paper was published in 2017. Since, then there was been additional papers of which the following are noteworthy Deep Multitask Gaze Estimation with a Constrained Landmark-Gaze Mode Recurrent CNN for 3D Gaze Estimation using Appearance and Shape Cues ...
This research delves into the intricate connection between self-attention mechanisms in large-scale pre-trained language models, like BERT, and human gaze
Learning-based methods are believed to work well for unconstrained gaze estimation, i.e. gaze estimation from a monocular RGB camera without assumptions regarding user, environment, or camera. However, current gaze datasets were collected under laboratory conditions and methods were not evaluated across...
By way of overview and example only, eye gaze estimation can refer to detecting a point (e.g., gaze point) in a given coordinate space at which an observer (e.g., such as a human or animal) is looking. For example, a camera can capture an image of a head, and using 3D landmar...