@InProceedings{HugoBertiche2021,
author="Hugo Bertiche
and Meysam Madadi
and Sergio Escalera",
title="Deep Parametric Surfaces for 3D Outfit Reconstruction from Single View Image",
booktitle="16th IEEE International Conference on Automatic Face and Gesture Recognition",
year="2021",
pages="1--8",
abstract="We present a methodology to retrieve analytical surfaces parametrized as a neural network. Previous works on 3D reconstruction yield point clouds, voxelized objects or meshes. Instead, our approach yields 2-manifolds in the euclidean space through deep learning. To this end, we implement a novel formulation for fully connected layers as parametrized manifolds that allows continuous predictions with differential geometry. Based on this property we propose a novel smoothness loss. Results on CLOTH3D++ dataset show the possibility to infer different topologies and the benefits of the smoothness term based on differential geometry.",
optnote="HUPBA; no proj",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3640), last updated on Mon, 24 Oct 2022 14:35:08 +0200",
doi="10.1109/FG52635.2021.9667017",
opturl="https://ieeexplore.ieee.org/document/9667017",
file=":http://refbase.cvc.uab.es/files/BME2021.pdf:PDF"
}