State of the Art on Monocular 3D Face Reconstruction, Tracking, and Applications

Simple item page
dc.contributor.authorZollhöfer, Michaelen_US
dc.contributor.authorThies, Justusen_US
dc.contributor.authorGarrido, Pabloen_US
dc.contributor.authorBradley, Dereken_US
dc.contributor.authorBeeler, Thaboen_US
dc.contributor.authorPérez, Patricken_US
dc.contributor.authorStamminger, Marcen_US
dc.contributor.authorNießner, Matthiasen_US
dc.contributor.authorTheobalt, Christianen_US
dc.contributor.editorHildebrandt, Klaus and Theobalt, Christianen_US
dc.date.accessioned2018-04-14T18:35:25Z
dc.date.available2018-04-14T18:35:25Z
dc.date.issued2018
dc.description.abstractThe computer graphics and vision communities have dedicated long standing efforts in building computerized tools for reconstructing, tracking, and analyzing human faces based on visual input. Over the past years rapid progress has been made, which led to novel and powerful algorithms that obtain impressive results even in the very challenging case of reconstruction from a single RGB or RGB-D camera. The range of applications is vast and steadily growing as these technologies are further improving in speed, accuracy, and ease of use. Motivated by this rapid progress, this state-of-the-art report summarizes recent trends in monocular facial performance capture and discusses its applications, which range from performance-based animation to real-time facial reenactment. We focus our discussion on methods where the central task is to recover and track a three dimensional model of the human face using optimization-based reconstruction algorithms. We provide an in-depth overview of the underlying concepts of real-world image formation, and we discuss common assumptions and simplifications that make these algorithms practical. In addition, we extensively cover the priors that are used to better constrain the under-constrained monocular reconstruction problem, and discuss the optimization techniques that are employed to recover dense, photo-geometric 3D face models from monocular 2D data. Finally, we discuss a variety of use cases for the reviewed algorithms in the context of motion capture, facial animation, as well as image and video editing.en_US
dc.description.documenttypestar
dc.description.number2
dc.description.sectionheadersState of the Art Reports
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume37
dc.identifier.doi10.1111/cgf.13382
dc.identifier.issn1467-8659
dc.identifier.pages523-550
dc.identifier.urihttps://doi.org/10.1111/cgf.13382
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13382
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectComputing methodologies
dc.subjectReconstruction
dc.subjectTracking
dc.subjectMotion capture
dc.subjectShape modeling
dc.subject3D imaging
dc.titleState of the Art on Monocular 3D Face Reconstruction, Tracking, and Applicationsen_US
Files
Collections
EG 2018 - STARs (CGF 37-2)