Adaptive Graphical Model Network for 2D Handpose Estimation
arXiv:1909.08205
Abstract
In this paper, we propose a new architecture called Adaptive Graphical Model Network (AGMN) to tackle the task of 2D hand pose estimation from a monocular RGB image. The AGMN consists of two branches of deep convolutional neural networks for calculating unary and pairwise potential functions, followed by a graphical model inference module for integrating unary and pairwise potentials. Unlike existing architectures proposed to combine DCNNs with graphical models, our AGMN is novel in that the parameters of its graphical model are conditioned on and fully adaptive to individual input images. Experiments show that our approach outperforms the state-of-the-art method used in 2D hand keypoints estimation by a notable margin on two public datasets. Code can be found at https://github.com/deyingk/agmn.
30th British Machine Vision Conference (BMVC)
References in corpus (5)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation
- Articulated Pose Estimation by a Graphical Model with Image Dependent Pairwise Relations
- Learning Deep Structured Models
- Geometric Pose Affordance: 3D Human Pose with Scene Constraints
Cited by in corpus (4)
- TransFusion: Cross-view Fusion with Transformer for 3D Human Pose Estimation
- Temporal-Aware Self-Supervised Learning for 3D Hand Pose and Mesh Estimation in Videos
- MVHM: A Large-Scale Multi-View Hand Mesh Benchmark for Accurate 3D Hand Pose Estimation
- Fast Monocular Hand Pose Estimation on Embedded Systems