321 citations · 337 across the 4 of their papers we have counts for
10 papers
HOnnotate: A method for 3D Annotation of Hand and Object Poses
Shreyas Hampali, Mahdi Rad, Markus Oberweger +1
We propose a method for annotating images of a hand manipulating an object with the 3D poses of both the hand and the object, together with a dataset created using this method. Our…
Generalized Feedback Loop for Joint Hand-Object Pose Estimation
Markus Oberweger, Paul Wohlhart, Vincent Lepetit
We propose an approach to estimating the 3D pose of a hand, possibly handling an object, given a depth image. We show that we can correct the mistakes made by a Convolutional Neura…
Domain Transfer for 3D Pose Estimation from Color Images without Manual Annotations
Mahdi Rad, Markus Oberweger, Vincent Lepetit
We introduce a novel learning method for 3D pose estimation from color images. While acquiring annotations for color images is a difficult task, our approach circumvents this probl…
Making Deep Heatmaps Robust to Partial Occlusions for 3D Object Pose Estimation
Markus Oberweger, Mahdi Rad, Vincent Lepetit
We introduce a novel method for robust and accurate 3D object pose estimation from a single color image under large occlusions. Following recent approaches, we first predict the 2D…
Feature Mapping for Learning Fast and Accurate 3D Pose Inference from Synthetic Images
Mahdi Rad, Markus Oberweger, Vincent Lepetit
We propose a simple and efficient method for exploiting synthetic images when training a Deep Network to predict a 3D pose from an image. The ability of using synthetic images for…
HandSeg: An Automatically Labeled Dataset for Hand Segmentation from Depth Images
Abhishake Kumar Bojja, Franziska Mueller, Sri Raghu Malireddi +5
We propose an automatic method for generating high-quality annotations for depth-based hand segmentation, and introduce a large-scale hand segmentation dataset. Existing datasets a…
DeepPrior++: Improving Fast and Accurate 3D Hand Pose Estimation
Markus Oberweger, Vincent Lepetit
DeepPrior is a simple approach based on Deep Learning that predicts the joint 3D locations of a hand given a depth map. Since its publication early 2015, it has been outperformed b…
Training a Feedback Loop for Hand Pose Estimation
Markus Oberweger, Paul Wohlhart, Vincent Lepetit
We propose an entirely data-driven approach to estimating the 3D pose of a hand given a depth image. We show that we can correct the mistakes made by a Convolutional Neural Network…
Efficiently Creating 3D Training Data for Fine Hand Pose Estimation
Markus Oberweger, Gernot Riegler, Paul Wohlhart +1
While many recent hand pose estimation methods critically rely on a training set of labelled frames, the creation of such a dataset is a challenging task that has been overlooked s…
Hands Deep in Deep Learning for Hand Pose Estimation
Markus Oberweger, Paul Wohlhart, Vincent Lepetit
We introduce and evaluate several architectures for Convolutional Neural Networks to predict the 3D joint locations of a hand given a depth map. We first show that a prior on the 3…