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Title: Real-time 3D face-eye performance capture of a person wearing VR headset
Authors: Song, Guoxian
Cai, Jianfei
Cham, Tat-Jen
Zheng, Jianmin
Zhang, Juyong
Fuchs, Henry
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Song, G., Cai, J., Cham, T.-J., Zheng, J., Zhang, J., & Fuchs, H. (2018). Real-time 3D face-eye performance capture of a person wearing VR headset. Proceedings of the 26th ACM international conference on Multimedia (MM '18), 923-931. doi:10.1145/3240508.3240570
Abstract: Teleconference or telepresence based on virtual reality (VR) head-mount display (HMD) device is a very interesting and promising application since HMD can provide immersive feelings for users. However, in order to facilitate face-to-face communications for HMD users, real-time 3D facial performance capture of a person wearing HMD is needed, which is a very challenging task due to the large occlusion caused by HMD. The existing limited solutions are very complex either in setting or in approach as well as lacking the performance capture of 3D eye gaze movement. In this paper, we propose a convolutional neural network (CNN) based solution for real-time 3D face-eye performance capture of HMD users without complex modification to devices. To address the issue of lacking training data, we generate massive pairs of HMD face-label dataset by data synthesis as well as collecting VR-IR eye dataset from multiple subjects. Then, we train a dense-fitting network for facial region and an eye gaze network to regress 3D eye model parameters. Extensive experimental results demonstrate that our system can efficiently and effectively produce in real time a vivid personalized 3D avatar with the correct identity, pose, expression and eye motion corresponding to the HMD user.
ISBN: 9781450356657
DOI: 10.1145/3240508.3240570
Rights: © 2018 Association for Computing Machinery. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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