Differentiable rasterization of minimum-time sigma-lognormal trajectories
| Type of publication: | Conference paper |
| Citation: | Berio_IGS_2025 |
| Publication status: | Published |
| Booktitle: | In Proc. 22nd Conference of the International Graphonomics Society (IGS) |
| Year: | 2025 |
| 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. |
| Main Research Program: | Human-AI Teaming |
| Keywords: | movement primitives, robot drawing |
| Projects: |
Idiap |
| Authors: | |
| Added by: | [UNK] |
| Total mark: | 0 |
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