PT Unknown
AU Hugo Bertiche
   Meysam Madadi
   Sergio Escalera
TI Deep Parametric Surfaces for 3D Outfit Reconstruction from Single View Image
BT 16th IEEE International Conference on Automatic Face and Gesture Recognition
PY 2021
BP 1
EP 8
DI 10.1109/FG52635.2021.9667017
AB 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.
ER