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 15, 2019

COMPUTER VISION

FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search | Facebook AI Research

Designing accurate and efficient ConvNets for mobile devices is challenging because the design space is combinatorially large. Due to this, previous neural architecture search (NAS) methods are computationally expensive. ConvNet architecture…

Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, Kurt Keutzer

June 15, 2019

April 28, 2019

COMPUTER VISION

Inverse Path Tracing for Joint Material and Lighting Estimation | Facebook AI Research

Modern computer vision algorithms have brought significant advancement to 3D geometry reconstruction. However, illumination and material reconstruction remain less studied, with current approaches assuming very simplified models for materials…

Dejan Azinović, Tzu-Mao Li, Anton Kaplanyan, Matthias Nießner

April 28, 2019

June 14, 2019

COMPUTER VISION

Thinking Outside the Pool: Active Training Image Creation for Relative Attributes | Facebook AI Research

Current wisdom suggests more labeled image data is always better, and obtaining labels is the bottleneck. Yet curating a pool of sufficiently diverse and informative images is itself a challenge. In particular, training image curation is…

Aron Yu, Kristen Grauman

June 14, 2019

September 09, 2018

COMPUTER VISION

DDRNet: Depth Map Denoising and Refinement for Consumer Depth Cameras Using Cascaded CNNs | Facebook AI Research

Consumer depth sensors are more and more popular and come to our daily lives marked by its recent integration in the latest iPhone X. However, they still suffer from heavy noises which dramatically limit their applications. Although plenty of…

Shi Yan, Chenglei Wu, Lizhen Wang, Feng Xu, Liang An, Kaiwen Guo, Yebin Liu

September 09, 2018

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.