Regularized Bundle-Adjustment to Model Heads from Image Sequences without Calibration Data
International Journal of Computer Vision
International Journal of Computer Vision
Modeling and Animating Realistic Faces from Images
International Journal of Computer Vision
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Extracting Facial Motion Parameters by Tracking Feature Points
AMCP '98 Proceedings of the First International Conference on Advanced Multimedia Content Processing
Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation
ACM SIGGRAPH 2006 Courses
Fast Non-Rigid Surface Detection, Registration and Realistic Augmentation
International Journal of Computer Vision
Design and implementation of intelligent tracing algorithm based on machine vision
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Accurate face models from uncalibrated and Ill-Lit video sequences
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Feature-Based Deformable Surface Detection with Self-Occlusion Reasoning
International Journal of Computer Vision
What is the average human face?
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
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We present a novel method for the shape and motion estimation of a deformable model using error residuals from model-based motion analysis. The motion of the model is first estimated using a model-based least squares method. Using the residuals from the least squares solution, the non-rigid structure of the model can be better estimated by computing how changes in the shape of the model affect its motion parameterization. This method is implemented as a component in a deformable model-based framework that uses optical flow information and edges. This general model-based framework is applied to human face shape and motion estimation. We present experiments that demonstrate that this framework is a considerable improvement over a framework that uses only optical flow information and edges.