Active shape models—their training and application
Computer Vision and Image Understanding
Realistic modeling for facial animation
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Expressive expression mapping with ratio images
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Trainable videorealistic speech animation
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Computer generated animation of faces
ACM '72 Proceedings of the ACM annual conference - Volume 1
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
MikeTalk: A Talking Facial Display Based on Morphing Visemes
CA '98 Proceedings of the Computer Animation
Modeling and animating realistic faces from images
Modeling and animating realistic faces from images
Multi-View Face Alignment Guided by Several Facial Feature Points
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
SVR-Based Facial Texture Driving for Realistic Expression Synthesis
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
Image-based photorealistic 3-D face modeling
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Realistic multi-view face animation with aid of 3D PDM
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
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Multi-view face animation is widely required in various applications nowadays, but most of existing relative techniques have burdensome computational load in data registration, model construction and animation. Addressing those problems, this paper proposes an efficient approach via employing quasi 3D model, which is the fusion of a 3D geometry model with 2D facial textures. 3D Point Distribute Model (PDM) serves to model geometrical deformation in the shape model. By preserving depth information, the proposed approach is convenient to manipulate pose variation and deal with new-coming subjects. By taking advantages of image-based techniques, data collection is facile and the number of our 3D model vertices is reduced to less than one hundred, while animated faces still keep expressive by incorporating with partial Expression Ratio Image (ERI). The primary experiments demonstrate that our approach efficiently achieved individual animated face among viewpoint range of [-60, 60] based on only 2 input facial images.