Simplification of 3D Morphable Models
Abstract
In this paper we show how to simplify a 3D morphable model. Our method only requires knowledge of the original highest resolution statistical model and leads to low resolution models in which the model statistics are a subset of the original high resolution model. We employ an iterative edge collapse strategy, where the deleted edge is chosen as a function of the model statistics. We show that the expected value of the Quadric Error Metric can be computed in closed form for a PCA deformable model. Model parameters obtained using the model at any resolution (lower) can be used to reconstruct a high resolution surface, providing a route to super-resolution. We provide experimental results for a statistical face model, showing how the simplified models improve the efficiency of model fitting. We are able to decrease the model resolution and fitting time by factors of approximately 10 and 4 respectively whilst inducing an error which is only slightly larger than the fitting error of the original model.
Cite
Text
Patel and Smith. "Simplification of 3D Morphable Models." IEEE/CVF International Conference on Computer Vision, 2011. doi:10.1109/ICCV.2011.6126252Markdown
[Patel and Smith. "Simplification of 3D Morphable Models." IEEE/CVF International Conference on Computer Vision, 2011.](https://mlanthology.org/iccv/2011/patel2011iccv-simplification/) doi:10.1109/ICCV.2011.6126252BibTeX
@inproceedings{patel2011iccv-simplification,
title = {{Simplification of 3D Morphable Models}},
author = {Patel, Ankur and Smith, William A. P.},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {2011},
pages = {271-278},
doi = {10.1109/ICCV.2011.6126252},
url = {https://mlanthology.org/iccv/2011/patel2011iccv-simplification/}
}