Automatic determination of facial muscle activations from sparse motion capture marker data

By Eftychios Sifakis, I. Neverov, Ronald Fedkiw. ACM Transactions on Graphics, 2005

424 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Benchmarking modern multiprocessors (2011, background): “It takes a model of a human face and a time sequence of muscle activations and computes a visually realistic animation of the modeled face by simulating the underlying physics [81, 88].”
Reconstruction of Personalized 3D Face Rigs from Monocular Video (2016, methodology): “Alternatively, physics-based muscle models can be used for animation control [Sifakis et al. 2005], either separately or in conjunction with a blendshape model.”
The PARSEC benchmark suite: Characterization and architectural implications (2008, background): “It computes a visually realistic animation of the modeled face by simulating the underlying physics [24] .”
Example-based facial rigging (2010, background): “Physically-based muscle models are well suited for creating realistic expression dynamics and secondary motions [Sifakis et al. 2005].”
Optimizing Locomotion Controllers Using Biologically-Based Actuators and Objectives (2012, methodology): “Such models have been used in facial animation [Waters 1987; Lee et al. 1995; Sifakis et al. 2005], simulation of the human hand [Sueda et al.”