2017
DOI: 10.1145/3130800.3130813
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Learning a model of facial shape and expression from 4D scans

Abstract: The field of 3D face modeling has a large gap between high-end and low-end methods. At the high end, the best facial animation is indistinguishable from real humans, but this comes at the cost of extensive manual labor. At the low end, face capture from consumer depth sensors relies on 3D face models that are not expressive enough to capture the variability in natural facial shape and expression. We seek a middle ground by learning a facial model from thousands of accurately aligned 3D scans. Our FLAME model (… Show more

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Cited by 817 publications
(632 citation statements)
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“…Li et al [18] recently introduced a generative model learned from a large collection of 3D motion sequences of faces. Pose changes due to skeletal motion is modeled using a skinning approach, while shape changes due to identity, expression, and pose correction are modeled as linear factors similar to 3DMM.…”
Section: Related Workmentioning
confidence: 99%
See 4 more Smart Citations
“…Li et al [18] recently introduced a generative model learned from a large collection of 3D motion sequences of faces. Pose changes due to skeletal motion is modeled using a skinning approach, while shape changes due to identity, expression, and pose correction are modeled as linear factors similar to 3DMM.…”
Section: Related Workmentioning
confidence: 99%
“…The input scans are registered using a non-rigid approach, e.g. [2,18], which enables reconstruction errors between the output meshes and the input scans to be estimated in a consistent way. These registrations need not be precise since they only serve as initialization and will be refined.…”
Section: Overviewmentioning
confidence: 99%
See 3 more Smart Citations