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Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals
Shanxin Yuan, Guillermo Garcia-Hernando, Bjorn Stenger +21
In this paper, we strive to answer two questions: What is the current state of 3D hand pose estimation from depth images? And, what are the next challenges that need to be tackled?…
Occlusion-aware Hand Pose Estimation Using Hierarchical Mixture Density Network
Qi Ye, Tae-Kyun Kim
Learning and predicting the pose parameters of a 3D hand model given an image, such as locations of hand joints, is challenging due to large viewpoint changes and articulations, an…
Learning Deep Convolutional Embeddings for Face Representation Using Joint Sample- and Set-based Supervision
Baris Gecer, Vassileios Balntas, Tae-Kyun Kim
In this work, we investigate several methods and strategies to learn deep embeddings for face recognition, using joint sample- and set-based optimization. We explain our framework…
The 2017 Hands in the Million Challenge on 3D Hand Pose Estimation
Shanxin Yuan, Qi Ye, Guillermo Garcia-Hernando +1
We present the 2017 Hands in the Million Challenge, a public competition designed for the evaluation of the task of 3D hand pose estimation. The goal of this challenge is to assess…
Recovering 6D Object Pose: A Review and Multi-modal Analysis
Caner Sahin, Tae-Kyun Kim
A large number of studies analyse object detection and pose estimation at visual level in 2D, discussing the effects of challenges such as occlusion, clutter, texture, etc., on the…
Deep Convolutional Decision Jungle for Image Classification
Seungryul Baek, Kwang In Kim, Tae-Kyun Kim
We propose a novel method called deep convolutional decision jungle (CDJ) and its learning algorithm for image classification. The CDJ maintains the structure of standard convoluti…
BigHand2.2M Benchmark: Hand Pose Dataset and State of the Art Analysis
Shanxin Yuan, Qi Ye, Bjorn Stenger +2
In this paper we introduce a large-scale hand pose dataset, collected using a novel capture method. Existing datasets are either generated synthetically or captured using depth sen…
First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations
Guillermo Garcia-Hernando, Shanxin Yuan, Seungryul Baek +1
In this work we study the use of 3D hand poses to recognize first-person dynamic hand actions interacting with 3D objects. Towards this goal, we collected RGB-D video sequences com…
Learning and Refining of Privileged Information-based RNNs for Action Recognition from Depth Sequences
Zhiyuan Shi, Tae-Kyun Kim
Existing RNN-based approaches for action recognition from depth sequences require either skeleton joints or hand-crafted depth features as inputs. An end-to-end manner, mapping fro…
A Learning-based Variable Size Part Extraction Architecture for 6D Object Pose Recovery in Depth
Caner Sahin, Rigas Kouskouridas, Tae-Kyun Kim
State-of-the-art techniques for 6D object pose recovery depend on occlusion-free point clouds to accurately register objects in 3D space. To deal with this shortcoming, we introduc…