Paper Info
Title
Model For Predicting Perception Of Facial Action Unit Activation Using Virtual Humans
Abstract
Blendshape facial rigs are used extensively in the industry for facial animation of virtual humans. How-ever, storing and manipulating large numbers of facial meshes (blendshapes) is costly in terms of mem-ory and computation for gaming applications. Blendshape rigs are comprised of sets of semantically-meaningful expressions, which govern how expressive the character will be, often based on Action Units from the Facial Action Coding System (FACS). However, the relative perceptual importance of blendshapes has not yet been investigated. Research in Psychology and Neuroscience has shown that our brains pro -cess faces differently than other objects so we postulate that the perception of facial expressions will be feature-dependent rather than based purely on the amount of movement required to make the ex-pression. Therefore, we believe that perception of blendshape visibility will not be reliably predicted by numerical calculations of the difference between the expression and the neutral mesh. In this paper, we explore the noticeability of blendshapes under different activation levels, and present new perceptually-based models to predict perceptual importance of blendshapes. The models predict visibility based on commonly-used geometry and image-based metrics. (c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/)
Year
DOI
Venue
2021
10.1016/j.cag.2021.07.022
COMPUTERS & GRAPHICS-UK
Keywords
DocType
Volume
Computers and graphics, Formatting, Guidelines
Journal
100
ISSN
Citations 
PageRank 
0097-8493
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Rachel McDonnell155849.37
Katja Zibrek27611.07
Emma Carrigan312.72
Rozenn Dahyot434032.62