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Learning 3D Head Pose From Synthetic Data: A Semi-Supervised Approach by Basak, Shubhajit (author);Corcoran, Peter (author);Khan, Faisal (author);Mcdonnell, Rachel (author);Schukat, Michael (author) is a Engineering article available to read on EtoBox.

What is Learning 3D Head Pose From Synthetic Data: A Semi-Supervised Approach about?

Accurate head pose estimation from 2D image data is an essential component of applications such as driver monitoring systems, virtual reality technology, and human-computer interaction. It enables a better determination of user engagement and attentiveness. The most accurate head pose estimators are based on Deep Neural Networks that are trained with the supervised approach and rely primarily on the accuracy of training data. The acquisition of real head pose data with a wide variation of yaw, pitch and roll is a challenging task. Publicly available head pose datasets have limitations with respect to size, resolution, annotation accuracy and diversity. In this work, a methodology is proposed to generate pixel-perfect synthetic 2D headshot images rendered from high-quality 3D synthetic facial models with accurate head pose annotations. A diverse range of variations in age, race, and gender are also provided. The resulting dataset includes more than 300k pairs of RGB images with corresponding head pose annotations. A wide range of variations in pose, illumination and background are included. The dataset is evaluated by training a state-of-the-art head pose estimation model and testin

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Author

Basak, Shubhajit (author);Corcoran, Peter (author);Khan, Faisal (author);Mcdonnell, Rachel (author);Schukat, Michael (author)

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2021

Language

EN

Field

Engineering (Physical Sciences)