Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop
Abstract
We introduce an automatic, end-to-end method for recovering the 3D pose and shape of dogs from monocular internet images. The large variation in shape between dog breeds, and significant occlusion and low quality of internet images makes this a challenging problem. We learn a richer prior over shapes than previous work, which helps regularize parameter estimation. We demonstrate results on the Stanford Dog Dataset, an ``in-the-wild'' dataset of 20,580 dog images for which we have collected 2D joint and silhouette annotations to split for training and evaluation. In order to capture the large shape variety of dogs, we show that the natural variation in the 2D dataset is enough to learn a detailed 3D prior through expectation maximisation (EM). As a by-product of training, we generate a new parameterised model (including limb scaling) SMBLD which we release alongside the annotation dataset to the research community.
Cite
Text
Biggs et al. "Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop." Proceedings of the European Conference on Computer Vision (ECCV), 2020. doi:10.1007/978-3-030-58621-8_12Markdown
[Biggs et al. "Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop." Proceedings of the European Conference on Computer Vision (ECCV), 2020.](https://mlanthology.org/eccv/2020/biggs2020eccv-left/) doi:10.1007/978-3-030-58621-8_12BibTeX
@inproceedings{biggs2020eccv-left,
title = {{Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop}},
author = {Biggs, Benjamin and Boyne, Oliver and Charles, James and Fitzgibbon, Andrew and Cipolla, Roberto},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2020},
doi = {10.1007/978-3-030-58621-8_12},
url = {https://mlanthology.org/eccv/2020/biggs2020eccv-left/}
}