# Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark

arXiv: https://arxiv.org/abs/2104.12668 · HTML: https://mlsanity.com/paper/2104.12668/

## Key facts

- Cited by 82 papers in the map (data through 14 Oct 2026).
- Builds on 48 of its 194 references that are in the map.
- First posted on arXiv 26 Apr 2021.

## Builds on (most cited first, 30 of 48)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Learning from Simulated and Unsupervised Images through Adversarial Training](https://mlsanity.com/paper/1612.07828/) (2016)
- [Towards Fast, Accurate and Stable 3D Dense Face Alignment](https://mlsanity.com/paper/2009.09960/) (2020)
- [Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network](https://mlsanity.com/paper/1803.07835/) (2018)
- [Gaze360: Physically Unconstrained Gaze Estimation in the Wild](https://mlsanity.com/paper/1910.10088/) (2019)
- [Eye Tracking for Everyone](https://mlsanity.com/paper/1606.05814/) (2016)
- [Appearance-Based Gaze Estimation in the Wild](https://mlsanity.com/paper/1504.02863/) (2015)
- [ETH-XGaze: A Large Scale Dataset for Gaze Estimation under Extreme Head Pose and Gaze Variation](https://mlsanity.com/paper/2007.15837/) (2020)
- [It's Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation](https://mlsanity.com/paper/1611.08860/) (2016)
- [MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation](https://mlsanity.com/paper/1711.09017/) (2017)
- [Predicting the Driver's Focus of Attention: the DR(eye)VE Project](https://mlsanity.com/paper/1705.03854/) (2017)
- [Few-Shot Adaptive Gaze Estimation](https://mlsanity.com/paper/1905.01941/) (2019)
- [A Coarse-to-Fine Adaptive Network for Appearance-Based Gaze Estimation](https://mlsanity.com/paper/2001.00187/) (2020)
- [PureGaze: Purifying Gaze Feature for Generalizable Gaze Estimation](https://mlsanity.com/paper/2103.13173/) (2021)
- [Connecting Gaze, Scene, and Attention: Generalized Attention Estimation via Joint Modeling of Gaze and Scene Saliency](https://mlsanity.com/paper/1807.10437/) (2018)
- [Rendering of Eyes for Eye-Shape Registration and Gaze Estimation](https://mlsanity.com/paper/1505.05916/) (2015)
- [Deep Pictorial Gaze Estimation](https://mlsanity.com/paper/1807.10002/) (2018)
- [Appearance-Based Gaze Estimation Using Dilated-Convolutions](https://mlsanity.com/paper/1903.07296/) (2019)
- [Deep Alignment Network: A convolutional neural network for robust face alignment](https://mlsanity.com/paper/1706.01789/) (2017)
- [Improving Few-Shot User-Specific Gaze Adaptation via Gaze Redirection Synthesis](https://mlsanity.com/paper/1904.10638/) (2019)
- [Unsupervised Representation Learning for Gaze Estimation](https://mlsanity.com/paper/1911.06939/) (2019)
- [Towards End-to-end Video-based Eye-Tracking](https://mlsanity.com/paper/2007.13120/) (2020)
- [LAEO-Net: revisiting people Looking At Each Other in videos](https://mlsanity.com/paper/1906.05261/) (2019)

## Built on by (most cited first, 30 of 82)

- [Gaze Estimation using Transformer](https://mlsanity.com/paper/2105.14424/) (2021)
- [L2CS-Net: Fine-Grained Gaze Estimation in Unconstrained Environments](https://mlsanity.com/paper/2203.03339/) (2022)
- [Challenges and Trends in Egocentric Vision: A Survey](https://mlsanity.com/paper/2503.15275/) (2025)
- [DVGaze: Dual-View Gaze Estimation](https://mlsanity.com/paper/2308.10310/) (2023)
- [What Do You See in Vehicle? Comprehensive Vision Solution for In-Vehicle Gaze Estimation](https://mlsanity.com/paper/2403.15664/) (2024)
- [Learning-by-Novel-View-Synthesis for Full-Face Appearance-Based 3D Gaze Estimation](https://mlsanity.com/paper/2201.07927/) (2022)
- [An End-to-End Review of Gaze Estimation and its Interactive Applications on Handheld Mobile Devices](https://mlsanity.com/paper/2307.00122/) (2023)
- [Eye-tracked Virtual Reality: A Comprehensive Survey on Methods and Privacy Challenges](https://mlsanity.com/paper/2305.14080/) (2023)
- [A Review of Driver Gaze Estimation and Application in Gaze Behavior Understanding](https://mlsanity.com/paper/2307.01470/) (2023)
- [3DGazeNet: Generalizing Gaze Estimation with Weak-Supervision from Synthetic Views](https://mlsanity.com/paper/2212.02997/) (2022)
- [Rotation-Constrained Cross-View Feature Fusion for Multi-View Appearance-based Gaze Estimation](https://mlsanity.com/paper/2305.12704/) (2023)
- [GazeGaussian: High-Fidelity Gaze Redirection with 3D Gaussian Splatting](https://mlsanity.com/paper/2411.12981/) (2024)
- [Enhancing 3D Gaze Estimation in the Wild using Weak Supervision with Gaze Following Labels](https://mlsanity.com/paper/2502.20249/) (2025)
- [Test-Time Personalization with Meta Prompt for Gaze Estimation](https://mlsanity.com/paper/2401.01577/) (2024)
- [UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training](https://mlsanity.com/paper/2502.02307/) (2025)
- [CrossGaze: A Strong Method for 3D Gaze Estimation in the Wild](https://mlsanity.com/paper/2402.08316/) (2024)
- [High-Fidelity Eye Animatable Neural Radiance Fields for Human Face](https://mlsanity.com/paper/2308.00773/) (2023)
- [Multi-Modal Gaze Following in Conversational Scenarios](https://mlsanity.com/paper/2311.05669/) (2023)
- [Quantifying the Impact of Motion on 2D Gaze Estimation in Real-World Mobile Interactions](https://mlsanity.com/paper/2502.10570/) (2025)
- [CUDA-GHR: Controllable Unsupervised Domain Adaptation for Gaze and Head Redirection](https://mlsanity.com/paper/2106.10852/) (2021)
- [FreeGaze: Resource-efficient Gaze Estimation via Frequency Domain Contrastive Learning](https://mlsanity.com/paper/2209.06692/) (2022)
- [Precise localization of corneal reflections in eye images using deep learning trained on synthetic data](https://mlsanity.com/paper/2304.05673/) (2023)
- [Toward Super-Resolution for Appearance-Based Gaze Estimation](https://mlsanity.com/paper/2303.10151/) (2023)
- [FR-Net:A Light-weight FFT Residual Net For Gaze Estimation](https://mlsanity.com/paper/2305.11875/) (2023)
- [Domain-Adaptive Full-Face Gaze Estimation via Novel-View-Synthesis and Feature Disentanglement](https://mlsanity.com/paper/2305.16140/) (2023)
- [TextGaze: Gaze-Controllable Face Generation with Natural Language](https://mlsanity.com/paper/2404.17486/) (2024)
- [Suppressing Uncertainty in Gaze Estimation](https://mlsanity.com/paper/2412.12890/) (2024)
- [3D Prior is All You Need: Cross-Task Few-shot 2D Gaze Estimation](https://mlsanity.com/paper/2502.04074/) (2025)
- [MAC-Gaze: Motion-Aware Continual Calibration for Mobile Gaze Tracking](https://mlsanity.com/paper/2505.22769/) (2025)
- [Investigation of Architectures and Receptive Fields for Appearance-based Gaze Estimation](https://mlsanity.com/paper/2308.09593/) (2023)

## Abstract (arXiv)

Human gaze provides valuable information on human focus and intentions, making it a crucial area of research. Recently, deep learning has revolutionized appearance-based gaze estimation. However, due to the unique features of gaze estimation research, such as the unfair comparison between 2D gaze positions and 3D gaze vectors and the different pre-processing and post-processing methods, there is a lack of a definitive guideline for developing deep learning-based gaze estimation algorithms. In this paper, we present a systematic review of the appearance-based gaze estimation methods using deep learning. Firstly, we survey the existing gaze estimation algorithms along the typical gaze estimation pipeline: deep feature extraction, deep learning model design, personal calibration and platforms. Secondly, to fairly compare the performance of different approaches, we summarize the data pre-processing and post-processing methods, including face/eye detection, data rectification, 2D/3D gaze conversion and gaze origin conversion. Finally, we set up a comprehensive benchmark for deep learning-based gaze estimation. We characterize all the public datasets and provide the source code of typical gaze estimation algorithms. 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. The project web page can be found at https://phi-ai.buaa.edu.cn/Gazehub.