Deep Shape Regression for Planar Curves with Multimodal Covariates
The shape of a planar curve is the geometric information that remains once translation, rotation, scale and reparametrisation are removed and is of interest in many health applications, e.g. in neuroimaging. We propose a deep shape regression model for open planar curves that admits multimodal and high-dimensional covariates. Representing curves as complex-valued functions, we show that the conditional full Procrustes mean is the leading eigenfunction of the conditional covariance. To estimate this covariance surface, we propose a novel deep conditional covariance smoother with modality-specific encoders - e.g. splines for scalar covariates and convolutional networks for images, which cla...
| Authors | Manuel Pfeuffer, Roshan Prakash Rane, Hadya Yassin et al. |
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| Primary category | stat.ME |
| Published | 21 July 2026 |
| Last updated at source | 21 July 2026 |
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