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Diffusion models for eye-gaze trajectory generation using position and velocity representations

Basnet, Laxman et al.
arXiv (All) · Papers
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artificial-intelligencecomputer-vision-and-pattern-recognitionneural-and-evolutionary-computing
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 generation of eye-gaze dy
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