Facial muscle parameter decision from 2D frontal image

Muscle based face image synthesis is one of the most realistic approaches to realizing life-like agents in a computer. A facial muscle model is composed of facial tissue elements and muscles. In this model, forces are calculated effecting facial tissue elements by contraction of each muscle strength, so the combination of each muscle parameter decides a specific facial expression. Each muscle parameter is decided based on a trial and error procedure comparing the sample photograph and generated image using our Muscle-Editor to generate a specific face image. We propose a strategy of automatic estimation of facial muscle parameters from 2D marker movements using a neural network. We can also carry out 3D motion estimation from 2D point or flow information in a captured image under restriction of a physics based face model