A low cost framework for real-time marker based 3-D human expression modeling

Alexander Woodward, Yuk Hin Chan, Rui Gong, Minh Nguyen, Trevor Gee, Patrice Delmas, Georgy Gimel’farb, Jorge Alberto Marquez Flores


This work presents a robust, and low-cost framework for real-time marker based 3-D human expression modeling using off-the-shelf stereo web-cameras and inexpensive adhesive markers applied to the face. The system has low computational requirements, runs on standard hardware, and is portable with minimal set-up time and no training. It does not require a controlled lab environment (lighting or set-up) and is robust undervarying conditions, i.e. illumination, facial hair, or skin tone variation. Stereo web-cameras perform 3-D marker tracking to obtain head rigid motion and the non-rigid motion of expressions. Tracked markers are then mapped onto a 3-D face model with a virtual muscle animation system. Muscle inverse kinematics update muscle contraction parameters based on marker motion in order to create a virtual character’s expression performance.

The parametrization of the muscle-based animation encodes a face performance with little bandwidth. Additionally, a radial basis function mapping approach was used to easily remap motion capture data to any face model. In this way the automated creation of a personalized 3-D face model and animation system from 3-D data is elucidated.

The expressive power of the system and its ability to recognize new expressions was evaluated on a group of test subjects with respect to the six universally recognized facial expressions. Results show that the use of abstract muscle definition reduces the effect of potential noise in the motion capture data and allows the seamless animation of any virtual anthropomorphic face model with data acquired through human face performance.


Facial motion capture; Marker based motion capture; Expression recognition; Low cost; Stereo vision

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