English(EN) Diffusion models for eye-gaze trajectory generation using position and velocity representations

扩散模型为研究和生物识别生成逼真的眼动轨迹

作者 PulseAugur 编辑部 · [3 个来源] · 2026-09-01 07:53

研究人员开发了新的扩散模型来生成逼真的眼动轨迹，解决了数据收集成本高昂且受隐私限制的挑战。一个模型专注于生成原始二维注视点位置，在运动学特征和注视持续时间方面实现了高精度。另一个模型合成了高频注视速度序列，并以身份、任务和主观状态为条件，在眼动生物识别等应用中显示出前景。 AI

影响合成注视数据生成方面的进步可以减少对昂贵硬件的依赖，并为生物识别和人机交互领域带来新应用。

排序理由两篇研究论文详细介绍了用于眼动轨迹生成的新型扩散模型。

AI 生成摘要 · Google Gemini · 来自 3 个来源。我们如何撰写摘要 →

本文如何被排名

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Newsworthiness bucket: Research; 两篇研究论文详细介绍了用于眼动轨迹生成的新型扩散模型。
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Topics: paper, model release; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
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完整方法见我们的编辑标准。

arXiv cs.AI TIER_1 English(EN) · Laxman Basnet, Alexander Szorkovszky, Pedro G. Lind, Anis Yazidi, Shailendra Bhandari · 2026-09-10 04:00

使用位置和速度表示的眼动轨迹生成扩散模型

arXiv:2609.05522v1 Announce Type: cross Abstract: Eye-tracking data are expensive to collect, requiring specialized hardware and controlled laboratory conditions, and difficult to share because of privacy constraints. We address this using two complementary denoising diffusion pr…
arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Shailendra Bhandari · 2026-09-01 07:53

使用位置和速度表示的眼动轨迹生成扩散模型

Eye-tracking data are expensive to collect, requiring specialized hardware and controlled laboratory conditions, and difficult to share because of privacy constraints. We address this using two complementary denoising diffusion probabilistic models (DDPMs) for unconditional gener…
arXiv cs.CV TIER_1 English(EN) · Kamrul Hasan, Mehedi Hasan Raju, Oleg V. Komogortsev · 2026-09-07 04:00

EyeMakeYou：用于高频注视合成的身份、任务和主观状态条件化扩散模型

arXiv:2609.04501v1 Announce Type: cross Abstract: Eye movement biometrics (EMB) is an emerging behavioral modality for user authentication, particularly in virtual- and augmented-reality systems, where gaze dynamics contain distinctive subject-specific features. However, robust E…