Open AccessPosted Content
OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
Reads0
Chats0
TLDR
OpenPose is released, the first open-source realtime system for multi-person 2D pose detection, including body, foot, hand, and facial keypoints, and the first combined body and foot keypoint detector, based on an internal annotated foot dataset.Abstract:
Realtime multi-person 2D pose estimation is a key component in enabling machines to have an understanding of people in images and videos. In this work, we present a realtime approach to detect the 2D pose of multiple people in an image. The proposed method uses a nonparametric representation, which we refer to as Part Affinity Fields (PAFs), to learn to associate body parts with individuals in the image. This bottom-up system achieves high accuracy and realtime performance, regardless of the number of people in the image. In previous work, PAFs and body part location estimation were refined simultaneously across training stages. We demonstrate that a PAF-only refinement rather than both PAF and body part location refinement results in a substantial increase in both runtime performance and accuracy. We also present the first combined body and foot keypoint detector, based on an internal annotated foot dataset that we have publicly released. We show that the combined detector not only reduces the inference time compared to running them sequentially, but also maintains the accuracy of each component individually. This work has culminated in the release of OpenPose, the first open-source realtime system for multi-person 2D pose detection, including body, foot, hand, and facial keypoints.read more
Citations
More filters
Journal ArticleDOI
Learning Object-Action Relations from Bimanual Human Demonstration Using Graph Networks
TL;DR: In this paper, a graph network classifier is trained using symbolic spatial object relations from raw RGB-D video data captured from the robot's point of view in order to build graph-based scene representations.
Proceedings Article
XNect: Real-time Multi-person 3D Human Pose Estimation with a Single RGB Camera
Dushyant Mehta,Oleksandr Sotnychenko,Franziska Mueller,Weipeng Xu,Mohamed Elgharib,Pascal Fua,Hans-Peter Seidel,Helge Rhodin,Gerard Pons-Moll,Christian Theobalt +9 more
TL;DR: In this paper, the SelecSLS Net is proposed to estimate 2D and 3D pose features along with identity assignments for all visible joints of all individuals in multi-person 3D motion capture.
Journal ArticleDOI
A Mixed-Perception Approach for Safe Human–Robot Collaboration in Industrial Automation
TL;DR: This work designs a reliable safety monitoring system for collaborative robots (cobots) using a combination of recognition of human actions using visual perception and at the same time interpreting physical human–robot contact by tactile perception to significantly enhance safety.
Posted Content
Dressing in Order: Recurrent Person Image Generation for Pose Transfer, Virtual Try-on and Outfit Editing
TL;DR: A flexible person generation framework called Dressing in Order (DiOr), which supports 2D pose transfer, virtual try-on, and several fashion editing tasks, and handles a wide range of editing functions for which there is no direct supervision.
Journal ArticleDOI
A survey on human-aware robot navigation
Ronja Möller,Antonino Furnari,Sebastiano Battiato,Aki Härmä,Giovanni Maria Farinella,Giovanni Maria Farinella +5 more
TL;DR: This paper is concerned with the navigation aspect of a socially compliant robot and provides a survey of existing solutions for the relevant areas of research as well as an outlook on possible future directions.
References
More filters
Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Book ChapterDOI
Microsoft COCO: Common Objects in Context
Tsung-Yi Lin,Michael Maire,Serge Belongie,James Hays,Pietro Perona,Deva Ramanan,Piotr Dollár,C. Lawrence Zitnick +7 more
TL;DR: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.
Proceedings ArticleDOI
Densely Connected Convolutional Networks
TL;DR: DenseNet as mentioned in this paper proposes to connect each layer to every other layer in a feed-forward fashion, which can alleviate the vanishing gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters.