Synthesizing realistic facial expressions from photographs
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
International Journal of Computer Vision
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
A Smoothing Filter for CONDENSATION
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
A Framework for Robust Subspace Learning
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
Robust Full-Motion Recovery of Head by Dynamic Templates and Re-Registration Techniques
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Multidimensional Morphable Models
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Efficient, Robust and Accurate Fitting of a 3D Morphable Model
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Multiclass Spectral Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Robust parameterized component analysis: theory and applications to 2D facial appearance models
Computer Vision and Image Understanding - Special issue on Face recognition
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
Adaptive view-based appearance models
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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Within the past few years, there has been a great interest in face modeling for analysis (e.g. facial expression recognition) and synthesis (e.g. virtual avatars). Two primary approaches are appearance models (AM) and structure from motion (SFM). While extensively studied, both approaches have limitations. We introduce a semi-automatic method for 3D facial appearance modeling from video that addresses previous problems. Four main novelties are proposed: • A 3D generative facial appearance model integrates both structure and appearance. • The model is learned in a semi-unsupervised manner from video sequences, greatly reducing the need for tedious manual pre-processing. • A constrained flow-based stochastic sampling technique improves specificity in the learning process. • In the appearance learning step, we automatically select the most representative images from the sequence. By doing so, we avoid biasing the linear model, speed up processing and enable more tractable computations. Preliminary experiments of learning 3D facial appearance models from video are reported.