Feature Boosting Network For 3D Pose Estimation

by   Jun Liu, et al.
Chalmers University of Technology
Nanyang Technological University
Peking University

In this paper, a feature boosting network is proposed for estimating 3D hand pose and 3D body pose from a single RGB image. In this method, the features learned by the convolutional layers are boosted with a new long short-term dependence-aware (LSTD) module, which enables the intermediate convolutional feature maps to perceive the graphical long short-term dependency among different hand (or body) parts using the designed Graphical ConvLSTM. Learning a set of features that are reliable and discriminatively representative of the pose of a hand (or body) part is difficult due to the ambiguities, texture and illumination variation, and self-occlusion in the real application of 3D pose estimation. To improve the reliability of the features for representing each body part and enhance the LSTD module, we further introduce a context consistency gate (CCG) in this paper, with which the convolutional feature maps are modulated according to their consistency with the context representations. We evaluate the proposed method on challenging benchmark datasets for 3D hand pose estimation and 3D full body pose estimation. Experimental results show the effectiveness of our method that achieves state-of-the-art performance on both of the tasks.


page 7

page 8


Pose Guided Structured Region Ensemble Network for Cascaded Hand Pose Estimation

Hand pose estimation from a single depth image is an essential topic in ...

Structured Feature Learning for Pose Estimation

In this paper, we propose a structured feature learning framework to rea...

Structured Context Enhancement Network for Mouse Pose Estimation

Automated analysis of mouse behaviours is crucial for many applications ...

A Novel Self-Intersection Penalty Term for Statistical Body Shape Models and Its Applications in 3D Pose Estimation

Statistical body shape models are widely used in 3D pose estimation due ...

HMTNet:3D Hand Pose Estimation from Single Depth Image Based on Hand Morphological Topology

Thanks to the rapid development of CNNs and depth sensors, great progres...

ChiNet: Deep Recurrent Convolutional Learning for Multimodal Spacecraft Pose Estimation

This paper presents an innovative deep learning pipeline which estimates...

Convolutional Pose Machines

Pose Machines provide a sequential prediction framework for learning ric...

Please sign up or login with your details

Forgot password? Click here to reset