Daniel Berio, Sylvain Calinon, Réjean Plamondon, Frederic Fol Leymarie

Differentiable Rasterization of Minimum-Time Sigma-Lognormal Trajectories

22nd Conference of the International Graphonomics Society

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Abstract

We present an adaptation of the sigma-lognormal model to generate and fit smooth trajectories in conjunction with a differentiable vector graphics (DiffVG) rendering pipeline and with parameter selection driven by a minimum-time smoothing criterion. This approach enables the incorporation of the ``Kinematic Theory of Rapid Human Movements’’ into modern image-based deep learning systems. We demonstrate its utility through various applications, including fitting handwriting trajectories to an image and generating trajectories using guidance from a large multimodal model.

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