Patch-based 3D Human Pose Refinement
arXiv:1905.08231
Abstract
State-of-the-art 3D human pose estimation approaches typically estimate pose from the entire RGB image in a single forward run. In this paper, we develop a post-processing step to refine 3D human pose estimation from body part patches. Using local patches as input has two advantages. First, the fine details around body parts are zoomed in to high resolution for preciser 3D pose prediction. Second, it enables the part appearance to be shared between poses to benefit rare poses. In order to acquire informative representation of patches, we explore different input modalities and validate the superiority of fusing predicted segmentation with RGB. We show that our method consistently boosts the accuracy of state-of-the-art 3D human pose methods.
Accepted by CVPR 2019 Augmented Human: Human-centric Understanding and 2D/3D Synthesis, and the third Look Into Person (LIP) Challenge Workshop
References in corpus (14)
- DeepPose: Human Pose Estimation via Deep Neural Networks
- VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera
- Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image
- Human pose estimation via Convolutional Part Heatmap Regression
- Articulated Pose Estimation by a Graphical Model with Image Dependent Pairwise Relations
- Hands Deep in Deep Learning for Hand Pose Estimation
- Pose Guided Structured Region Ensemble Network for Cascaded Hand Pose Estimation
- Model-based Deep Hand Pose Estimation
- BodyNet: Volumetric Inference of 3D Human Body Shapes
- OriNet: A Fully Convolutional Network for 3D Human Pose Estimation
- Integral Human Pose Regression
- Holistic Planimetric prediction to Local Volumetric prediction for 3D Human Pose Estimation
- DeepSkeleton: Skeleton Map for 3D Human Pose Regression
- Ordinal Depth Supervision for 3D Human Pose Estimation