OriNet: A Fully Convolutional Network for 3D Human Pose Estimation
arXiv:1811.04989
Abstract
In this paper, we propose a fully convolutional network for 3D human pose estimation from monocular images. We use limb orientations as a new way to represent 3D poses and bind the orientation together with the bounding box of each limb region to better associate images and predictions. The 3D orientations are modeled jointly with 2D keypoint detections. Without additional constraints, this simple method can achieve good results on several large-scale benchmarks. Further experiments show that our method can generalize well to novel scenes and is robust to inaccurate bounding boxes.
arXiv commentBMVC 2018. Code available at https://github.com/chenxuluo/OriNet-demo
References in corpus (6)
- DeepPose: Human Pose Estimation via Deep Neural Networks
- Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image
- Stacked Hourglass Networks for Human Pose Estimation
- MoCap-guided Data Augmentation for 3D Pose Estimation in the Wild
- It's all Relative: Monocular 3D Human Pose Estimation from Weakly Supervised Data
- MonoCap: Monocular Human Motion Capture using a CNN Coupled with a Geometric Prior
Cited by in corpus (11)
- Monocular Total Capture: Posing Face, Body, and Hands in the Wild
- SMPLR: Deep SMPL reverse for 3D human pose and shape recovery
- A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and Pose
- Lifting 2D Human Pose to 3D with Domain Adapted 3D Body Concept
- SRNet: Improving Generalization in 3D Human Pose Estimation with a Split-and-Recombine Approach
- PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation
- MonoClothCap: Towards Temporally Coherent Clothing Capture from Monocular RGB Video
- Patch-based 3D Human Pose Refinement
- Context Modeling in 3D Human Pose Estimation: A Unified Perspective
- Hierarchical Graph Networks for 3D Human Pose Estimation
- 3D Human Pose Estimation Based on 2D-3D Consistency with Synchronized Adversarial Training