Physiological Fusion Net: Quantifying Individual VR Sickness with Content Stimulus and Physiological Response
Deep Objective Assessment Model Based On Spatio-Temporal Perception Of 360-Degree Video For VR Sickness Prediction
VRSA Net: VR Sickness Assessment considering Exceptional Motion for 360-degree VR Video
Binocular Fusion Net: Deep Learning Visual Comfort Assessment for Stereoscopic 3D
VR IQA NET: Deep Virtual Reality Image Quality Assessment using Adversarial Learning
Measurement of exceptional motion in VR video contents for VR sickness assessment using deep convolutional autoencoder
Multi-view Stereoscopic Video Hole Filling Considering Spatio-Temporal Consistency and Binocular Symmetry for Synthesized 3D Video
Image and Video System Lab, School of Electrical Engineering, KAIST
This web page provides the datasets of our ICASSP 2018 paper [1], so that researchers can repeat our
experiments or test our facial point detector on other datasets. The datasets is for research purposes
only. If you use our datasets, please cite the paper [1].
ⓒ2018 IMAGE & Video SYSTEMS LAB DB.
KOREA ADVANCED IN STITUTE OF SCIENCE AND TECHNOLOGY(KAIST) Tel. +82-42-350-5494, 8094
335 Gwahak-ro(373-1 Guseong-dong). Yuseong-gu, Daejeon 305-701,Republic of Korea
