Search Results for author:
Found 10 papers, 4 papers with code
HOnnotate: A method for 3D Annotation of Hand and Object Poses
This dataset is currently made of 77, 558 frames, 68 sequences, 10 persons, and 10 objects.
Generalized Feedback Loop for Joint Hand-Object Pose Estimation
We show that we can correct the mistakes made by a Convolutional Neural Network trained to predict an estimate of the 3D pose by using a feedback loop.
Domain Transfer for 3D Pose Estimation from Color Images without Manual Annotations
We introduce a novel learning method for 3D pose estimation from color images.
Making Deep Heatmaps Robust to Partial Occlusions for 3D Object Pose Estimation
We introduce a novel method for robust and accurate 3D object pose estimation from a single color image under large occlusions.
Feature Mapping for Learning Fast and Accurate 3D Pose Inference from Synthetic Images
The ability of using synthetic images for training a Deep Network is extremely valuable as it is easy to create a virtually infinite training set made of such images, while capturing and annotating real images can be very cumbersome.
HandSeg: An Automatically Labeled Dataset for Hand Segmentation from Depth Images
We propose an automatic method for generating high-quality annotations for depth-based hand segmentation, and introduce a large-scale hand segmentation dataset.
DeepPrior++: Improving Fast and Accurate 3D Hand Pose Estimation
DeepPrior is a simple approach based on Deep Learning that predicts the joint 3D locations of a hand given a depth map.
Ranked #10 on
Hand Pose Estimation
on MSRA Hands
Training a Feedback Loop for Hand Pose Estimation
We propose an entirely data-driven approach to estimating the 3D pose of a hand given a depth image.
Efficiently Creating 3D Training Data for Fine Hand Pose Estimation
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 so far.
Hands Deep in Deep Learning for Hand Pose Estimation
We introduce and evaluate several architectures for Convolutional Neural Networks to predict the 3D joint locations of a hand given a depth map.
