Landmark-Guided Deformation Transfer of Template Facial Expressions for Automatic Generation of Avatar Blendshapes

Onizuka, Hayato; Thomas, Diego; Uchiyama, Hideaki; Taniguchi, Rin-ichiro

doi:10.1109/iccvw.2019.00265

Cited by 11 publications

(1 citation statement)

References 18 publications

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“…Sumner and Popovió [2004] propose deformation transfer for generic 3D triangle mesh. Onizuka et al [2019] propose a landmark-guided deformation transfer method to generate expressions for any target avatar that directly maps to a generic blendshape template. These methods can generate an expression for a novel subject but might fail to capture personalized behavior due to the lack of personal information.…”

Section: Related Workmentioning

confidence: 99%

Dynamic facial asset and rig generation from a single scan

Kuang

Zhao

et al. 2020

ACM Trans. Graph.

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The creation of high-fidelity computer-generated (CG) characters for films and games is tied with intensive manual labor, which involves the creation of comprehensive facial assets that are often captured using complex hardware. To simplify and accelerate this digitization process, we propose a framework for the automatic generation of high-quality dynamic facial models, including rigs which can be readily deployed for artists to polish. Our framework takes a single scan as input to generate a set of personalized blendshapes, dynamic textures, as well as secondary facial components ( e.g. , teeth and eyeballs). Based on a facial database with over 4, 000 scans with pore-level details, varying expressions and identities, we adopt a self-supervised neural network to learn personalized blendshapes from a set of template expressions. We also model the joint distribution between identities and expressions, enabling the inference of a full set of personalized blendshapes with dynamic appearances from a single neutral input scan. Our generated personalized face rig assets are seamlessly compatible with professional production pipelines for facial animation and rendering. We demonstrate a highly robust and effective framework on a wide range of subjects, and showcase high-fidelity facial animations with automatically generated personalized dynamic textures.

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Section: Related Workmentioning

confidence: 99%