Performance-driven facial animation
SIGGRAPH '90 Proceedings of the 17th annual conference on Computer graphics and interactive techniques
Animating images with drawings
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
Realistic modeling for facial animation
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Computer facial animation
Synthesizing realistic facial expressions from photographs
Proceedings of the 25th 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
Geometry-based muscle modeling for facial animation
GRIN'01 No description on Graphics interface 2001
An example-based approach for facial expression cloning
Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation
Geometry-driven photorealistic facial expression synthesis
Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation
Linear algebra operators for GPU implementation of numerical algorithms
ACM SIGGRAPH 2003 Papers
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Deformation transfer for triangle meshes
ACM SIGGRAPH 2004 Papers
Spacetime faces: high resolution capture for modeling and animation
ACM SIGGRAPH 2004 Papers
Geometry-Driven Photorealistic Facial Expression Synthesis
IEEE Transactions on Visualization and Computer Graphics
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We present a learning-based 3D facial expression mapping technique that preserves facial expression details and works in real time for even high resolution meshes. Our approach is inspired by a previously developed technique called deformation transfer [1]. The deformation transfer technique preserves facial expression details but its computational overhead makes it not suitable for real time applications. To accelerate computation, we use a piecewise linear function to represent the mapping from the direct motion (the difference between the expression face and the neutral face) to the motion obtained by the deformation transfer method. This piecewise linear function is learned offline from a small set of training data. The online computation is thus reduced to the evaluation of the piecewise linear functions which is significantly faster. As a result, we are able to perform real time expression mapping for even high resolution 3D meshes.