ABSTRACT
We present a framework for automatically generating personalized blendshapes from actor performance measurements, while preserving the semantics of a template facial animation rig. Firstly, we capture various poses from the subject with our photogrammetry apparatus. The 3D reconstruction from each pose is then corresponded by an image-based tracking algorithm. The core of our framework is an optimization algorithm which iteratively refines the initial estimation of the blendshapes such that they can fit the performance measurements better. This framework facilitates creation of an ensemble of realistic digital-double face rigs for each individual with consistent behavior across the character set.
Supplemental Material
Available for Download
Supplemental files.
- Beeler, T., Hahn, F., Bradley, D., Bickel, B., Beardsley, P., Gotsman, C., Sumner, R. W., and Gross, M. 2011. High-quality passive facial performance capture using anchor frames. ACM Trans. Graph. 30, 4, 75:1--75:10. Google ScholarDigital Library
- Botsch, M., and Sorkine, O. 2008. On linear variational surface deformation methods. IEEE Trans. on Visualization and Computer Graphics 14, 1, 213--230. Google ScholarDigital Library
- Hyneman, W., Itokazu, H., Williams, L., and Zhao, X. 2005. Human face project. In ACM SIGGRAPH 2005 Courses. Google ScholarDigital Library
- Kazemi, V., and Sullivan, J. 2014. One millisecond face alignment with an ensemble of regression trees. In Proc. CVPR, 1867--1874. Google ScholarDigital Library
- Kobbelt, L., Vorsatz, J., and Seidel, H.-P. 1999. Multiresolution hierarchies on unstructured triangle meshes. Computational Geometry 14, 1, 5--24. Google ScholarDigital Library
- Li, H., Weise, T., and Pauly, M. 2010. Example-based facial rigging. ACM Trans. Graph. 29, 4, 32:1--32:6. Google ScholarDigital Library
- Sagonas, C., Antonakos, E., Tzimiropoulos, G., Zafeiriou, S., and Pantic, M. 2015. 300 faces in-the-wild challenge: database and results. Image and Vision Computing. Google ScholarDigital Library
- Sorkine, O., and Alexa, M. 2007. As-rigid-as-possible surface modeling. In Proc. SCA, SGP '07, 109--116. Google ScholarDigital Library
- Sumner, R., and Popović, J. 2004. Deformation transfer for triangle meshes. ACM Trans. Graph. 23, 3, 399--405. Google ScholarDigital Library
- Weise, T., Li, H., Van Gool, L., and Pauly, M. 2009. Face/off: Live facial puppetry. In Proc. SCA, 7--16. Google ScholarDigital Library
- Weise, T., Bouaziz, S., Li, H., and Pauly, M. 2011. Realtime performance-based facial animation. ACM Trans. Graph. 30, 4, 77:1--77:10. Google ScholarDigital Library
- Wenger, A., Gardner, A., Tchou, C., Unger, J., Hawkins, T., and Debevec, P. 2005. Performance relighting and reflectance transformation with time-multiplexed illumination. ACM Trans. Graph. 24, 3, 756--764. Google ScholarDigital Library
Index Terms
- Semantically-aware blendshape rigs from facial performance measurements
Recommendations
Facial retargeting with automatic range of motion alignment
While facial capturing focuses on accurate reconstruction of an actor's performance, facial animation retargeting has the goal to transfer the animation to another character, such that the semantic meaning of the animation remains. Because of the ...
Interactive editing of performance-based facial animation
SA '19: SIGGRAPH Asia 2019 Technical BriefsWhile performance-based facial animation efficiently produces realistic animation, it still needs additional editing after automatic solving and retargeting. We review why additional editing is required and present a set of interactive editing solutions ...
Muscle-based facial retargeting with anatomical constraints
SIGGRAPH '19: ACM SIGGRAPH 2019 TalksWe present a physically based facial retargeting algorithm that is suitable for use in high-end production. Given an actor's facial performance, we first run a targeted muscle simulation on the actor in order to determine the actor blendshape muscles ...
Comments