3.6k citations · 4.7k across the 31 of their papers we have counts for
6 papers · 1 filter
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…
Deeper Image Quality Transfer: Training Low-Memory Neural Networks for 3D Images
Stefano B. Blumberg, Ryutaro Tanno, Iasonas Kokkinos +1
In this paper we address the memory demands that come with the processing of 3-dimensional, high-resolution, multi-channeled medical images in deep learning. We exploit memory-effi…
Deep Spatio-Temporal Random Fields for Efficient Video Segmentation
Siddhartha Chandra, Camille Couprie, Iasonas Kokkinos
In this work we introduce a time- and memory-efficient method for structured prediction that couples neuron decisions across both space at time. We show that we are able to perform…
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…
DensePose: Dense Human Pose Estimation In The Wild
Rıza Alp Güler, Natalia Neverova, Iasonas Kokkinos
In this work, we establish dense correspondences between RGB image and a surface-based representation of the human body, a task we refer to as dense human pose estimation. We first…