Shape Morphing as a Minimal Path in the Graph of Cubified Shapes

Raphaël Groscot, Laurent Cohen

2023

Abstract

The systematic study of morphings for non parametric shapes suffers from ambiguities in defining good general morphings, such as the trade-off between plausibility and smoothness, above all under large topology changes. In the recent years, only neural networks have offered a generic solution, using their latent space as a shape prior. But these models are optimized for single shape reconstruction, giving little control on the generated morphings. In this paper, we show how qualitatively similar results can be achieved when replacing neural networks with a set of carefully crafted components: a style-content separation method via the fitting of a Deformable Voxel Grid, a similarity metric adapted to the extracted content, and a formulation of morphings as minimal paths in a graph. While forgoing the automatic learning of a generative model, we still achieve similar morphing capabilities. We performed various evaluations, quantitative analysis on the robustness of our proposed method and on the quality of the results, and demonstrate the usefulness of each component. Finally, we provide guidance on how manual intervention can improve quality. This is indeed possible since, unlike neural networks, each component in our method is interpretable.

Paper Citation

in Harvard Style

Groscot R. and Cohen L. (2023). Shape Morphing as a Minimal Path in the Graph of Cubified Shapes. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 1: GRAPP; ISBN 978-989-758-634-7, SciTePress, pages 98-109. DOI: 10.5220/0011680200003417

in Bibtex Style

@conference{grapp23,
author={Raphaël Groscot and Laurent Cohen},
title={Shape Morphing as a Minimal Path in the Graph of Cubified Shapes},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 1: GRAPP},
year={2023},
pages={98-109},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011680200003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 1: GRAPP
TI - Shape Morphing as a Minimal Path in the Graph of Cubified Shapes
SN - 978-989-758-634-7
AU - Groscot R.
AU - Cohen L.
PY - 2023
SP - 98
EP - 109
DO - 10.5220/0011680200003417
PB - SciTePress