Deep learnable spectral decomposition of 3D baby faces
In this paper, we introduce a novel, deep 3D morphable model for meshes with common triangulation. Specifically, we apply it to reconstruct baby faces. The proposed algorithm is simple, adaptable, and specifically targeted to perform well on small datasets. We combine Graph-Laplacian based spectral...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Consorcio Madroño |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:dnet:recercat____::392ec6754d8f08fe8c8d4b0a98bd9a2b |
| Acceso en línea: | https://hdl.handle.net/10230/73082 http://dx.doi.org/10.1016/j.patcog.2025.112180 |
| Access Level: | acceso abierto |
| Palabra clave: | 3D autoencoder Meshes Baby faces Limited data Augmentation |
| Sumario: | In this paper, we introduce a novel, deep 3D morphable model for meshes with common triangulation. Specifically, we apply it to reconstruct baby faces. The proposed algorithm is simple, adaptable, and specifically targeted to perform well on small datasets. We combine Graph-Laplacian based spectral decomposition with a learnable, transformer-like component. The decomposition matrices are applied as skip-connections, providing our architecture with a prior that encodes both local and global information of the underlying mesh structure. The learnable component does not make any domain-specific assumptions and can override the prior, if necessary. This flexibility also allows our model to perform well on larger datasets. We further modify the decomposition matrices to create deeper versions of this architecture and introduce a data augmentation strategy: flipping and rotations are applied to the deviations from the mean, rather than directly to the samples. In our experiments, we compare the reconstruction error of the proposed architecture against the state of the art, examine the effect of data augmentation across a small baby face dataset and a larger adult dataset and inspect our model's capabilities to generate new samples from the encoded distribution. We show that our method outperforms current baby face models, as well as state of the art 3D morphable models, especially on the raw data. Additionally, we demonstrate that the proposed data augmentation substantially improves existing models. |
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