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Learnable Triangulation of Human Pose
TL;DR: Two novel solutions for multi-view 3D human pose estimation based on new learnable triangulation methods that combine 3D information from multiple 2D views are presented and end-to-end differentiable, which allows us to directly optimize the target metric.
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Abstract: We present two novel solutions for multi-view 3D human pose estimation based on new learnable triangulation methods that combine 3D information from multiple 2D views. The first (baseline) solution is a basic differentiable algebraic triangulation with an addition of confidence weights estimated from the input images. The second solution is based on a novel method of volumetric aggregation from intermediate 2D backbone feature maps. The aggregated volume is then refined via 3D convolutions that produce final 3D joint heatmaps and allow modelling a human pose prior. Crucially, both approaches are end-to-end differentiable, which allows us to directly optimize the target metric. We demonstrate transferability of the solutions across datasets and considerably improve the multi-view state of the art on the Human3.6M dataset. Video demonstration, annotations and additional materials will be posted on our project page (this https URL).
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Citations
Reliability and accuracy of 2D lower limb joint angles during a standing-up motion for markerless motion analysis software using deep learning
TL;DR: In this paper , the authors compared OpenPose (OP) and DeepLabCut (DLC) for estimating the joint angles of the hip and knee joints during standing movements with an estimation error of fewer than 10°.
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A Unified Multi-view Multi-person Tracking Framework
TL;DR: In this article , a unified multi-view multi-person tracking framework is proposed to bridge the gap between footprint tracking and pose tracking by adopting monocular 2D bounding boxes and 2D poses as input to produce robust 3D trajectories for multiple persons.
Simple yet effective 3D ego-pose lift-up based on vector and distance for a mounted omnidirectional camera
Teppei Miura,Shinji Sako +1 more
TL;DR: Li et al. as discussed by the authors proposed a 3D ego-pose estimation from a single mounted omnidirectional camera that captures the entire circumference by back-to-back dual fisheye cameras.
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2D Human Pose Estimation Calibration and Keypoint Visibility Classification
Zhongyu Jiang,Haorui Ji,Cheng Yen Yang,Jenq-Neng Hwang +3 more
- 14 Apr 2024
TL;DR: This paper proposes a new 2D human pose estimation calibration method that not only enhances the accuracy of 2D pose estimation but also aligns the confidence scores with the quality and visibility of keypoints.
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ElliPose: Stereoscopic 3D Human Pose Estimation by Fitting Ellipsoids
Christian Grund,Julian Tanke +1 more
- 01 Jan 2023
TL;DR: In this paper , the authors propose ElliPose, a Stereoscopic 3D Human Pose Estimation by Fitting Ellipsoids, where they jointly estimate the 3D human as well as the camera pose.
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Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments
TL;DR: A new dataset, Human3.6M, of 3.6 Million accurate 3D Human poses, acquired by recording the performance of 5 female and 6 male subjects, under 4 different viewpoints, is introduced for training realistic human sensing systems and for evaluating the next generation of human pose estimation models and algorithms.