# Few-Shot Adaptive Gaze Estimation

arXiv: https://arxiv.org/abs/1905.01941 · HTML: https://mlsanity.com/paper/1905.01941/

## Key facts

- Cited by 77 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2019.

## Built on by (most cited first, 30 of 77)

- [Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark](https://mlsanity.com/paper/2104.12668/) (2021)
- [PureGaze: Purifying Gaze Feature for Generalizable Gaze Estimation](https://mlsanity.com/paper/2103.13173/) (2021)
- [Generalizing Gaze Estimation with Outlier-guided Collaborative Adaptation](https://mlsanity.com/paper/2107.13780/) (2021)
- [Automatic Gaze Analysis: A Survey of Deep Learning based Approaches](https://mlsanity.com/paper/2108.05479/) (2021)
- [Weakly-Supervised Physically Unconstrained Gaze Estimation](https://mlsanity.com/paper/2105.09803/) (2021)
- [Patch-level Gaze Distribution Prediction for Gaze Following](https://mlsanity.com/paper/2211.11062/) (2022)
- [TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation](https://mlsanity.com/paper/2106.06326/) (2021)
- [LARGE: Latent-Based Regression through GAN Semantics](https://mlsanity.com/paper/2107.11186/) (2021)
- [Contrastive Representation Learning for Gaze Estimation](https://mlsanity.com/paper/2210.13404/) (2022)
- [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)
- [GazeNeRF: 3D-Aware Gaze Redirection with Neural Radiance Fields](https://mlsanity.com/paper/2212.04823/) (2022)
- [Spatio-Temporal Attention and Gaussian Processes for Personalized Video Gaze Estimation](https://mlsanity.com/paper/2404.05215/) (2024)
- [3DGazeNet: Generalizing Gaze Estimation with Weak-Supervision from Synthetic Views](https://mlsanity.com/paper/2212.02997/) (2022)
- [EFE: End-to-end Frame-to-Gaze Estimation](https://mlsanity.com/paper/2305.05526/) (2023)
- [MTGLS: Multi-Task Gaze Estimation with Limited Supervision](https://mlsanity.com/paper/2110.12100/) (2021)
- [Free-HeadGAN: Neural Talking Head Synthesis with Explicit Gaze Control](https://mlsanity.com/paper/2208.02210/) (2022)
- [NeRF-Gaze: A Head-Eye Redirection Parametric Model for Gaze Estimation](https://mlsanity.com/paper/2212.14710/) (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)
- [LatentGaze: Cross-Domain Gaze Estimation through Gaze-Aware Analytic Latent Code Manipulation](https://mlsanity.com/paper/2209.10171/) (2022)
- [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)
- [Towards Self-Supervised Gaze Estimation](https://mlsanity.com/paper/2203.10974/) (2022)
- [Jitter Does Matter: Adapting Gaze Estimation to New Domains](https://mlsanity.com/paper/2210.02082/) (2022)
- [ReDirTrans: Latent-to-Latent Translation for Gaze and Head Redirection](https://mlsanity.com/paper/2305.11452/) (2023)
- [High-Fidelity Eye Animatable Neural Radiance Fields for Human Face](https://mlsanity.com/paper/2308.00773/) (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)
- [The Story in Your Eyes: An Individual-difference-aware Model for Cross-person Gaze Estimation](https://mlsanity.com/paper/2106.14183/) (2021)

## Abstract (arXiv)

Inter-personal anatomical differences limit the accuracy of person-independent gaze estimation networks. Yet there is a need to lower gaze errors further to enable applications requiring higher quality. Further gains can be achieved by personalizing gaze networks, ideally with few calibration samples. However, over-parameterized neural networks are not amenable to learning from few examples as they can quickly over-fit. We embrace these challenges and propose a novel framework for Few-shot Adaptive GaZE Estimation (FAZE) for learning person-specific gaze networks with very few (less than or equal to 9) calibration samples. FAZE learns a rotation-aware latent representation of gaze via a disentangling encoder-decoder architecture along with a highly adaptable gaze estimator trained using meta-learning. It is capable of adapting to any new person to yield significant performance gains with as few as 3 samples, yielding state-of-the-art performance of 3.18 degrees on GazeCapture, a 19% improvement over prior art. We open-source our code at https://github.com/NVlabs/few_shot_gaze