Articulated Pose Estimation by a Graphical Model with Image Dependent Pairwise Relations

07/12/2014
by   Xianjie Chen, et al.
0

We present a method for estimating articulated human pose from a single static image based on a graphical model with novel pairwise relations that make adaptive use of local image measurements. More precisely, we specify a graphical model for human pose which exploits the fact the local image measurements can be used both to detect parts (or joints) and also to predict the spatial relationships between them (Image Dependent Pairwise Relations). These spatial relationships are represented by a mixture model. We use Deep Convolutional Neural Networks (DCNNs) to learn conditional probabilities for the presence of parts and their spatial relationships within image patches. Hence our model combines the representational flexibility of graphical models with the efficiency and statistical power of DCNNs. Our method significantly outperforms the state of the art methods on the LSP and FLIC datasets and also performs very well on the Buffy dataset without any training.

READ FULL TEXT

page 2

page 8

research
09/18/2019

Adaptive Graphical Model Network for 2D Handpose Estimation

In this paper, we propose a new architecture called Adaptive Graphical M...
research
04/27/2015

Combining Local Appearance and Holistic View: Dual-Source Deep Neural Networks for Human Pose Estimation

We propose a new learning-based method for estimating 2D human pose from...
research
05/23/2018

3D Human Pose Estimation with Relational Networks

In this paper, we propose a novel 3D human pose estimation algorithm fro...
research
12/13/2015

Articulated Pose Estimation Using Hierarchical Exemplar-Based Models

Exemplar-based models have achieved great success on localizing the part...
research
08/18/2023

Improving 3D Pose Estimation for Sign Language

This work addresses 3D human pose reconstruction in single images. We pr...
research
11/17/2014

Relations World: A Possibilistic Graphical Model

We explore the idea of using a "possibilistic graphical model" as the ba...
research
02/16/2019

Deep Convolutional Sum-Product Networks for Probabilistic Image Representations

Sum-Product Networks (SPNs) are hierarchical probabilistic graphical mod...

Please sign up or login with your details

Forgot password? Click here to reset