3 citations · 3 across the 1 of their papers we have counts for
5 papers · 1 filter
Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild
Dominik Kulon, Riza Alp Güler, Iasonas Kokkinos +2
We introduce a simple and effective network architecture for monocular 3D hand pose estimation consisting of an image encoder followed by a mesh convolutional decoder that is train…
Slim DensePose: Thrifty Learning from Sparse Annotations and Motion Cues
Natalia Neverova, James Thewlis, Rıza Alp Güler +2
DensePose supersedes traditional landmark detectors by densely mapping image pixels to body surface coordinates. This power, however, comes at a greatly increased annotation time,…
Dense Pose Transfer
Natalia Neverova, Riza Alp Guler, Iasonas Kokkinos
In this work we integrate ideas from surface-based modeling with neural synthesis: we propose a combination of surface-based pose estimation and deep generative models that allows…
Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance
Zhixin Shu, Mihir Sahasrabudhe, Alp Guler +3
In this work we introduce Deforming Autoencoders, a generative model for images that disentangles shape from appearance in an unsupervised manner. As in the deformable template par…
DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild
Riza Alp Guler, Yuxiang Zhou, George Trigeorgis +4
In this work we use deep learning to establish dense correspondences between a 3D object model and an image "in the wild". We introduce "DenseReg", a fully-convolutional neural net…