3.6k citations · 4.7k across the 31 of their papers we have counts for
36 papers · 1 filter
MeshPose: Unifying DensePose and 3D Body Mesh reconstruction
Eric-Tuan Lê, Antonis Kakolyris, Petros Koutras +5
DensePose provides a pixel-accurate association of images with 3D mesh coordinates, but does not provide a 3D mesh, while Human Mesh Reconstruction (HMR) systems have high 2D repro…
Deformably-Scaled Transposed Convolution
Stefano B. Blumberg, Daniele Raví, Mou-Cheng Xu +3
Transposed convolution is crucial for generating high-resolution outputs, yet has received little attention compared to convolution layers. In this work we revisit transposed convo…
Beyond Deterministic Translation for Unsupervised Domain Adaptation
Eleni Chiou, Eleftheria Panagiotaki, Iasonas Kokkinos
In this work we challenge the common approach of using a one-to-one mapping ('translation') between the source and target domains in unsupervised domain adaptation (UDA). Instead,…
To The Point: Correspondence-driven monocular 3D category reconstruction
Filippos Kokkinos, Iasonas Kokkinos
We present To The Point (TTP), a method for reconstructing 3D objects from a single image using 2D to 3D correspondences learned from weak supervision. We recover a 3D shape from a…
Learning monocular 3D reconstruction of articulated categories from motion
Filippos Kokkinos, Iasonas Kokkinos
Monocular 3D reconstruction of articulated object categories is challenging due to the lack of training data and the inherent ill-posedness of the problem. In this work we use vide…
Harnessing Uncertainty in Domain Adaptation for MRI Prostate Lesion Segmentation
Eleni Chiou, Francesco Giganti, Shonit Punwani +2
The need for training data can impede the adoption of novel imaging modalities for learning-based medical image analysis. Domain adaptation methods partially mitigate this problem…
Holistic Multi-View Building Analysis in the Wild with Projection Pooling
Zbigniew Wojna, Krzysztof Maziarz, Łukasz Jocz +3
We address six different classification tasks related to fine-grained building attributes: construction type, number of floors, pitch and geometry of the roof, facade material, and…
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…
Going Deeper with Lean Point Networks
Eric-Tuan Le, Iasonas Kokkinos, Niloy J. Mitra
In this work we introduce Lean Point Networks (LPNs) to train deeper and more accurate point processing networks by relying on three novel point processing blocks that improve memo…
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,…
Lifting AutoEncoders: Unsupervised Learning of a Fully-Disentangled 3D Morphable Model using Deep Non-Rigid Structure from Motion
Mihir Sahasrabudhe, Zhixin Shu, Edward Bartrum +3
In this work we introduce Lifting Autoencoders, a generative 3D surface-based model of object categories. We bring together ideas from non-rigid structure from motion, image format…
Attentive Single-Tasking of Multiple Tasks
Kevis-Kokitsi Maninis, Ilija Radosavovic, Iasonas Kokkinos
In this work we address task interference in universal networks by considering that a network is trained on multiple tasks, but performs one task at a time, an approach we refer to…
MultiGrain: a unified image embedding for classes and instances
Maxim Berman, Hervé Jégou, Andrea Vedaldi +2
MultiGrain is a network architecture producing compact vector representations that are suited both for image classification and particular object retrieval. It builds on a standard…
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…
Segmentation-Aware Convolutional Networks Using Local Attention Masks
Adam W. Harley, Konstantinos G. Derpanis, Iasonas Kokkinos
We introduce an approach to integrate segmentation information within a convolutional neural network (CNN). This counter-acts the tendency of CNNs to smooth information across regi…
Mass Displacement Networks
Natalia Neverova, Iasonas Kokkinos
Despite the large improvements in performance attained by using deep learning in computer vision, one can often further improve results with some additional post-processing that ex…
DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild
Rıza Alp Güler, George Trigeorgis, Epameinondas Antonakos +3
In this paper we propose to learn a mapping from image pixels into a dense template grid through a fully convolutional network. We formulate this task as a regression problem and t…
Deep, Dense, and Low-Rank Gaussian Conditional Random Fields
Siddhartha Chandra, Iasonas Kokkinos
In this work we introduce a fully-connected graph structure in the Deep Gaussian Conditional Random Field (G-CRF) model. For this we express the pairwise interactions between pixel…
UberNet: Training a `Universal' Convolutional Neural Network for Low-, Mid-, and High-Level Vision using Diverse Datasets and Limited Memory
Iasonas Kokkinos
In this work we introduce a convolutional neural network (CNN) that jointly handles low-, mid-, and high-level vision tasks in a unified architecture that is trained end-to-end. Su…