Human Pose Estimation with Spatial Contextual Information

Abstract

We explore the importance of spatial contextual information in human pose estimation. Most state-of-the-art pose networks are trained in a multi-stage manner and produce several auxiliary predictions for deep supervision. With this principle, we present two conceptually simple and yet computational efficient modules, namely Cascade Prediction Fusion (CPF) and Pose Graph Neural Network (PGNN), to exploit underlying contextual information. Cascade prediction fusion accumulates prediction maps from previous stages to extract informative signals. The resulting maps also function as a prior to guide prediction at following stages. To promote spatial correlation among joints, our PGNN learns a structured representation of human pose as a graph. Direct message passing between different joints is enabled and spatial relation is captured. These two modules require very limited computational complexity. Experimental results demonstrate that our method consistently outperforms previous methods on MPII and LSP benchmark.

References in corpus (5)

• Semi-Supervised Classification with Graph Convolutional Networks
• Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation
• Self Adversarial Training for Human Pose Estimation
• CRF-CNN: Modeling Structured Information in Human Pose Estimation
• An Expressive Deep Model for Human Action Parsing from A Single Image

Cited by in corpus (16)

• Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future
• Rethinking on Multi-Stage Networks for Human Pose Estimation
• TFPose: Direct Human Pose Estimation with Transformers
• Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression
• Cascade Feature Aggregation for Human Pose Estimation
• OmniPose: A Multi-Scale Framework for Multi-Person Pose Estimation
• Differentiable Hierarchical Graph Grouping for Multi-Person Pose Estimation
• Graph-PCNN: Two Stage Human Pose Estimation with Graph Pose Refinement
• SPCNet:Spatial Preserve and Content-aware Network for Human Pose Estimation
• Global Context for Convolutional Pose Machines
• TRB: A Novel Triplet Representation for Understanding 2D Human Body
• Anti-Confusing: Region-Aware Network for Human Pose Estimation
• Single Person Pose Estimation: A Survey
• Adversarial Semantic Data Augmentation for Human Pose Estimation
• An Adversarial Human Pose Estimation Network Injected with Graph Structure
• Self-Supervision and Spatial-Sequential Attention Based Loss for Multi-Person Pose Estimation