Human pose estimation in videos
Mar 25, 2019 · Human Pose Estimation is an evolving discipline with opportunity for research across various fronts. Recently, there has been a noticeable trend in Human Pose Estimation of moving towards the use of deep learning, specifically CNN based approaches, due to their superior performance across tasks and datasets. XNect: Real-time Multi-person 3D Human Pose Estimation with a Single RGB Camera. 1 Jul 2019 • osmr/imgclsmob • . In the second stage, a fully-connected neural network turns the possibly partial (on account of occlusion) 2D pose and 3D pose features for each subject into a complete 3D pose estimate per individual.
Despite a long history of research, human pose estima-tion in videos remains a very challenging task in computer vision. Compared to still image pose estimation, the tem-poral component of videos provides an additional (and im-portant) cue for recognition, as strong dependencies of pose positions exist between temporally close video frames. 3. Tree-based Optimization for Human Pose Estimation in Videos We formulate the video based human pose estimation problem into a uniﬁed tree-based optimization framework, which can be solved efﬁciently by dynamic programming. In view of the major steps shown in Fig.3, we introduce the general notions of relational and hypothesis graphs, Anatomy-aware 3D Human Pose Estimation in Videos. 02/24/2020 ∙ by Tianlang Chen, et al. ∙ 13 ∙ share In this work, we propose a new solution for 3D human pose estimation in videos. Instead of directly regressing the 3D joint locations, we draw inspiration from the human skeleton anatomy and decompose the task into bone direction ... Human Pose Estimation from Video and IMUs Article in IEEE Transactions on Pattern Analysis and Machine Intelligence 38(8):1-1 · February 2016 with 158 Reads How we measure 'reads'
Nov 28, 2018 · In this work, we demonstrate that 3D poses in video can be effectively estimated with a fully convolutional model based on dilated temporal convolutions over 2D keypoints. We also introduce back-projection, a simple and effective semi-supervised training method that leverages unlabeled video data. We start with predicted 2D keypoints for unlabeled video, then estimate 3D poses and finally back ...
Occlusion-Aware Networks for 3D Human Pose Estimation in Video Yu Cheng∗1, Bo Yang∗2, Bo Wang2, Wending Yan1, and Robby T. Tan1,3 1National University of Singapore 2Tencent Game AI Research Center 3D human pose estimation in videos has been widely studied in recent years. It has extensive applications in ac-tion recognition, sports analysis and human-computer inter-action. Current state-of-the-art approaches [24, 14, 4] typ-ically decompose the task into 2D keypoint detection fol-lowed by 3D pose estimation. Given an input video, they Only a few works in pose estimation have exploited human motion and, in particular, several methods [23,24] use optical flow constraints to improve 2D human pose estimation in videos.
Human Pose estimation is an important problem and has enjoyed the attention of the Computer Vision community for the past few decades. It is an important step towards understanding people in images and videos. In this post, I write about the basics of Human Pose Estimation (2D) and review the literature on this topic.