Papers › L2CS-Net: Fine-Grained Gaze Estimation in Unconstrained Environments

L2CS-Net: Fine-Grained Gaze Estimation in Unconstrained Environments

7 Mar 2022arXiv:2203.03339archive 2025-07-28

Ahmed A. Abdelrahman, Thorsten Hempel, Aly Khalifa, Ayoub Al-Hamadi

Human gaze is a crucial cue used in various applications such as human-robot interaction and virtual reality. Recently, convolution neural network (CNN) approaches have made notable progress in predicting gaze direction. However, estimating gaze in-the-wild is still a challenging problem due to the uniqueness of eye appearance, lightning conditions, and the diversity of head pose and gaze directions. In this paper, we propose a robust CNN-based model for predicting gaze in unconstrained settings. We propose to regress each gaze angle separately to improve the per-angel prediction accuracy, which will enhance the overall gaze performance. In addition, we use two identical losses, one for each angle, to improve network learning and increase its generalization. We evaluate our model with two popular datasets collected with unconstrained settings. Our proposed model achieves state-of-the-art accuracy of 3.92{\deg} and 10.41{\deg} on MPIIGaze and Gaze360 datasets, respectively. We make our code open source at https://github.com/Ahmednull/L2CS-Net.

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Code

Syntology Ran 5 of 10 code samples harvested from 2 repositories linked to this paper; 5 have no recorded run. Of those that ran: 1 ran · honoured contract; 3 ran · our draft was wrong; 1 ran with no contract checked.

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ahmednull/l2cs-net mentioned in papermentioned on GitHubpytorchMIT report

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1ran · honoured contract
3ran · our draft was wrong
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atoi ahmednull/l2cs-net/l2cs/utils.py named in the paper ran · honoured contract fingerprinted MIT (permissive) · 6dbf1b2a5b901370 · report
natural_keys ahmednull/l2cs-net/l2cs/utils.py named in the paper ran fingerprinted MIT (permissive) · 7052cf0436949e37 · report
draw_bbox ahmednull/l2cs-net/l2cs/vis.py named in the paper unverified MIT (permissive) · 1db8413dd5010d6d · report
draw_gaze ahmednull/l2cs-net/l2cs/vis.py named in the paper unverified MIT (permissive) · c245dc2db42c51e1 · report
prep_input_numpy ahmednull/l2cs-net/l2cs/utils.py named in the paper unverified MIT (permissive) · bf642780353748df · report
reparameterize_model yakhyo/gaze-estimation/models/mobileone.py community (archive-listed) ran · our draft was wrong MIT (permissive) · 4283b387669166bd · report
create_mobileone_model yakhyo/gaze-estimation/models/mobileone.py community (archive-listed) unverified MIT (permissive) · 4bddc67420125c05 · report
mobileone_s0 yakhyo/gaze-estimation/models/mobileone.py community (archive-listed) unverified MIT (permissive) · 4df4b0135ebb8b1f · report
conv1x1 identical code first harvested elsewhere ran · our draft was wrong licence of this copy not recorded · d97ed054442099c9 · report
conv3x3 identical code first harvested elsewhere ran · our draft was wrong licence of this copy not recorded · 7f22d053fb2c79d3 · report

Tasks

DiversityGaze EstimationGaze Prediction

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Gaze Estimation Gaze360 L2CS Angular Error 10.41 #2 of 4 Archive leaderboard report
Gaze Estimation MPII Gaze L2CS Angular Error 3.92 #3 of 6 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Convolution

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