DeepSkeleton: Skeleton Map for 3D Human Pose Regression
arXiv:1711.10796
Abstract
Despite recent success on 2D human pose estimation, 3D human pose estimation still remains an open problem. A key challenge is the ill-posed depth ambiguity nature. This paper presents a novel intermediate feature representation named skeleton map for regression. It distills structural context from irrelavant properties of RGB image e.g. illumination and texture. It is simple, clean and can be easily generated via deconvolution network. For the first time, we show that training regression network from skeleton map alone is capable of meeting the performance of state-of-theart 3D human pose estimation works. We further exploit the power of multiple 3D hypothesis generation to obtain reasonbale 3D pose in consistent with 2D pose detection. The effectiveness of our approach is validated on challenging in-the-wild dataset MPII and indoor dataset Human3.6M.
References in corpus (24)
- Deep Residual Learning for Image Recognition
- Caffe: Convolutional Architecture for Fast Feature Embedding
- Fully Convolutional Networks for Semantic Segmentation
- Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image
- Human pose estimation via Convolutional Part Heatmap Regression
- Stacked Hourglass Networks for Human Pose Estimation
- Convolutional Pose Machines
- Human Pose Regression by Combining Indirect Part Detection and Contextual Information
- A simple yet effective baseline for 3d human pose estimation
- Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach
- RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
- Structured Prediction of 3D Human Pose with Deep Neural Networks
- Heterogeneous Multi-task Learning for Human Pose Estimation with Deep Convolutional Neural Network
- Sparseness Meets Deepness: 3D Human Pose Estimation from Monocular Video
- Fully Convolutional Instance-aware Semantic Segmentation
- Human Pose Estimation using Deep Consensus Voting
- Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision
- Learning to Fuse 2D and 3D Image Cues for Monocular Body Pose Estimation
- 3D Human Pose Estimation from a Single Image via Distance Matrix Regression
- 3D Human Pose Estimation = 2D Pose Estimation + Matching
- Zoom Better to See Clearer: Human and Object Parsing with Hierarchical Auto-Zoom Net
- Pose2Instance: Harnessing Keypoints for Person Instance Segmentation
- Joint Multi-Person Pose Estimation and Semantic Part Segmentation
- Human Pose Estimation using Global and Local Normalization