Performance-driven hand-drawn animation
NPAR '00 Proceedings of the 1st international symposium on Non-photorealistic animation and rendering
Speech-driven cartoon animation with emotions
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Modeling and Animating Realistic Faces from Images
International Journal of Computer Vision
Fully Automatic Upper Facial Action Recognition
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Performance-driven hand-drawn animation
ACM SIGGRAPH 2006 Courses
A robust multimodal approach for emotion recognition
Neurocomputing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Realtime performance-based facial animation
ACM SIGGRAPH 2011 papers
Robust and accurate shape model fitting using random forest regression voting
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
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Eigen-points estimates the image-plane locations of fiduciary points on an objects. By estimating multiple locations simultaneously, eigen-points exploits the inter-dependence between these locations. This is done by associating neighboring, inter-dependent control-points with a model of the local appearance. The model of local appearance is used to find the feature in new unlabeled images. Control-point locations are then estimated from the appearance of this feature in the unlabeled image. The estimation is done using an affine manifold model of the coupling between the local appearance and the local shape. Eigen-points uses models aimed specifically at recovering shape from image appearance. The estimation equations are solved non-iteratively, in a way that accounts for noise in the training data and the unlabeled images and that accounts for uncertainty in the distribution and dependencies within these noise sources.