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arXiv Computer Vision

Real-time Appearance-based Gaze Estimation for Open Domains

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Hugging Face Trending Papers
Aug 11

Gaze Target Estimation Anywhere with Concepts

Estimating human gaze targets from images in-the-wild is an important and formidable task. Existing approaches primarily employ brittle, multi-stage pipelines that require explicit inputs, like head bounding boxes and human pose, in order to identify the subject of gaze analysis.

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arXiv Computer Vision
6d ago

OpenVAM: Open-World Visual Attention Modeling with VLMs

OpenVAM is a new framework for visual attention modeling that combines a dense saliency map with language‑based explanations. It uses a decoupled design: a visual pathway for precise localization and a vision‑language head that generates grounded what/why explanations. The method is trained in three stages to preserve localization while adding language grounding, and a scalable pipeline creates multi‑domain annotations for evaluation.

By Kiana Hooshanfar, Amirhossein Kazerouni, Alireza Hosseini, Michael Brudno, Babak Taati
roboticssafety
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arXiv Computer Vision
Sep 7

EyeMakeYou: Identity-, Task-, and Subjective-State-Conditioned Diffusion for High-Frequency Gaze Synthesis

EyeMakeYou is a multi‑conditional denoising diffusion model that synthesizes high‑frequency, subject‑specific gaze velocity sequences. It conditions on identity, task, and self‑reported subjective states (difficulty, mental tiredness, eye tiredness) to generate realistic 5‑second, 1000‑Hz bivariate gaze data from a reference trajectory. Experiments on the GazeBase dataset show that EyeMakeYou outperforms existing generative methods in spatial accuracy and real‑synthetic similarity while preserving task‑dependent associations with subjective reports.

By Kamrul Hasan, Mehedi Hasan Raju, Oleg V. Komogortsev
ragdiffusion
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arXiv Computer Vision
Sep 17

GazeDiT: Gaze-Accurate Diffusion Image Generation for Eye Tracking via Spatial Conditioning

GazeDiT is a diffusion model that generates synthetic eye images conditioned on a precise 4‑dimensional gaze vector by using a spatial condition derived from pupil and iris geometry. During training, a frozen SegFormer extracts this geometry from real images, while inference employs a physical eye renderer to produce gaze‑consistent geometries without needing a source image. The model achieves lower tail gaze‑label error than other diffusion baselines and improves downstream eye‑tracking accuracy, reducing error on challenging cases from 3.05° to 2.80°.

By Dongze Wu, David Colmenares, Fengting Yang, Jogendra Nath Kundu, Yao Xie, Ali Behrooz, Conny Lu
diffusion
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arXiv Computer Vision
3d ago

GazeFlow: From Human Gaze Behavior to Generative Egocentric Gaze Prediction

GazeFlow is a new framework for egocentric gaze prediction that models gaze as a joint distribution of temporal positions conditioned on both top‑down task cues and bottom‑up visual saliency. It employs conditional flow matching to iteratively transform Gaussian noise into realistic gaze trajectories, using a velocity field informed by video‑encoded visual features and global task queries. On standard benchmarks, GazeFlow outperforms existing methods on per‑frame metrics and produces trajectories that better reflect human gaze dynamics.

By Sheng Zhao, Weikai Lin, Yuhao Zhu
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arXiv AI
Sep 10

Emo-DVS: A Multimodal Benchmark for Privacy-Aware Emotion Recognition with Event Cameras

The paper introduces Emo-DVS, a large-scale, multimodal dataset combining event camera, audio, and text data for emotion recognition, designed to mitigate privacy concerns associated with RGB cameras. It proposes the Information‑Guided Gated Fusion (IGF) framework, which pre‑trains an event encoder on the dataset’s FAU subset, adaptively gates modalities to reduce noise, and aligns cross‑modal representations via mutual information maximization. Experiments show that IGF outperforms existing methods on this challenging tri‑modal benchmark.

By Jiaqi Chen, Qinfu Xu, Hao Zhuang, Liyuan Pan
computer-visionmultimodalbenchmarkssafety
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arXiv AI
Sep 1

AOI-Net: Structural Face AOI-Guided Eye-Gaze Track Representation Learning for Autism Spectrum Disorder Detection

AOI-Net introduces a structural face AOI-guided Eye‑Gaze Track Network that jointly models short‑term temporal dynamics and AOI‑level structural organization for Autism Spectrum Disorder detection. The network uses a gating mechanism to adaptively combine complementary representations and incorporates class‑distribution‑aware learning to address the imbalance between ASD and typically developing participants. Experiments on a large clinical eye‑tracking database with over 1,300 participants demonstrate that AOI‑Net outperforms state‑of‑the‑art methods and offers interpretable gaze‑behavior modeling for scalable AI‑driven ASD screening.

By Zhanpei Huang, Binbin Sun, Jialiang Chen, Yiou Wang, Taochen Chen, Yuzhu Ji, Yiqun Zhang, Yiu-Ming Cheung
ragbenchmarks
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arXiv Computer Vision
Sep 21

OpenSAL360: Open-Source Crowdsourcing Platform for Omnidirectional Video Saliency Collection

OpenSAL360 is an open‑source platform that enables scalable, low‑cost collection of 360° video saliency data using only a standard screen, mouse, and internet connection. It bypasses the need for VR headsets, allowing parallel data collection from crowdsourced assessors. The authors validated the protocol against seven VR eye‑tracking datasets, performed ablation studies, and released a new dataset of 500 omnidirectional videos annotated by over 2,000 assessors, the largest in the field to date.

By Alexey Bryncev, Andrey Moskalenko, Kira Shilovskaya, Ivan Kosmynin, Dmitriy Vatolin
benchmarks
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