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Recent Advances in Facial Appearance Capture
Author(s) -
Klehm Oliver,
Rousselle Fabrice,
Papas Marios,
Bradley Derek,
Hery Christophe,
Bickel Bernd,
Jarosz Wojciech,
Beeler Thabo
Publication year - 2015
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.12594
Subject(s) - computer science , rendering (computer graphics) , artificial intelligence , motion capture , computer vision , parametric statistics , active appearance model , face (sociological concept) , human–computer interaction , computer graphics (images) , image (mathematics) , mathematics , sociology , motion (physics) , social science , statistics
Facial appearance capture is now firmly established within academic research and used extensively across various application domains, perhaps most prominently in the entertainment industry through the design of virtual characters in video games and films. While significant progress has occurred over the last two decades, no single survey currently exists that discusses the similarities, differences, and practical considerations of the available appearance capture techniques as applied to human faces. A central difficulty of facial appearance capture is the way light interacts with skin—which has a complex multi‐layered structure—and the interactions that occur below the skin surface can, by definition, only be observed indirectly. In this report, we distinguish between two broad strategies for dealing with this complexity. “Image‐based methods” try to exhaustively capture the exact face appearance under different lighting and viewing conditions, and then render the face through weighted image combinations. “Parametric methods” instead fit the captured reflectance data to some parametric appearance model used during rendering, allowing for a more lightweight and flexible representation but at the cost of potentially increased rendering complexity or inexact reproduction. The goal of this report is to provide an overview that can guide practitioners and researchers in assessing the tradeoffs between current approaches and identifying directions for future advances in facial appearance capture.