Research

Computer Vision

Single-Network Whole-Body Pose Estimation

October 27, 2019

Abstract

We present the first single-network approach for 2D whole-body pose estimation, which entails simultaneous localization of body, face, hands, and feet keypoints. Due to the bottom-up formulation, our method maintains constant real-time performance regardless of the number of people in the image. The network is trained in a single stage using multi-task learning, through an improved architecture which can handle scale differences between body/foot and face/hand keypoints. Our approach considerably improves upon OpenPose [9], the only work so far capable of whole-body pose estimation, both in terms of speed and global accuracy. Unlike [9], our method does not need to run an additional network for each hand and face candidate, making it substantially faster for multi-person scenarios. This work directly results in a reduction of computational complexity for applications that require 2D whole-body information (e.g., VR/AR, re-targeting). In addition, it yields higher accuracy, especially for occluded, blurry, and low resolution faces and hands. For code, trained models, and validation benchmarks, visit our project page.

Download the Paper

Related Publications

June 17, 2019

Computer Vision

Graph-Based Global Reasoning Networks | Facebook AI Research

Yunpeng Chen, Marcus Rohrbach, Zhicheng Yan, Shuicheng Yan, Jiashi Feng, Yannis Kalantidis

June 17, 2019

June 17, 2019

Computer Vision

DMC-Net: Generating Discriminative Motion Cues for Fast Compressed Video Action Recognition | Facebook AI Research

Zheng Shou, Xudong Lin, Yannis Kalantidis, Laura Sevilla-Lara, Marcus Rohrbach, Shih-Fu Chang, Zhicheng Yan

June 17, 2019

June 18, 2019

Computer Vision

Embodied Question Answering in Photorealistic Environments with Point Cloud Perception | Facebook AI Research

Erik Wijmans, Samyak Datta, Oleksandr Maksymets, Abhishek Das, Georgia Gkioxari, Stefan Lee, Irfan Essa, Devi Parikh, Dhruv Batra

June 18, 2019

July 28, 2019

Speech & Audio

Computer Vision

Learning to Optimize Halide with Tree Search and Random Programs | Facebook AI Research

Andrew Adams, Karima Ma, Luke Anderson, Riyadh Baghdadi, Tzu-Mao Li, Michaël Gharbi, Benoit Steiner, Steven Johnson, Kayvon Fatahalian, Frédo Durand, Jonathan Ragan-Kelley

July 28, 2019

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.