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Exchanging Faces in Images. SIGGRAPH ’04 Blanz V., Scherbaum K., Vetter T., Seidel HP. Speaker: Alvin Date: 21 July 2004. Outline. Introduction Morphable Models Estimation Exchanging Faces Compositing Application Results Conclusions. Introduction. - PowerPoint PPT Presentation
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Exchanging Faces in Images
SIGGRAPH ’04Blanz V., Scherbaum K., Vetter T., Seidel
HP.Speaker: Alvin
Date: 21 July 2004
Alivn/GAME Lab./CSIE/NDHU
Exchanging Faces in Images 2
OutlineIntroductionMorphable ModelsEstimationExchanging FacesCompositingApplicationResultsConclusions
Alivn/GAME Lab./CSIE/NDHU
Exchanging Faces in Images 3
Introduction
Pasting somebody’s face into an existing image.A novel type of image manipulation:
Always needs pairs of images with the same viewpoint and the same illumination.The system only need one image, and can across large differences in viewpoint and illumination.
Alivn/GAME Lab./CSIE/NDHU
Exchanging Faces in Images 4
Introduction (cont.)
Manual interaction:Click on a set of about 7 feature points.Mark the hairline in the target image.
ExampleTwo applications:
Virtual try-on for hairstylesFace recognition
Alivn/GAME Lab./CSIE/NDHU
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Previous Works
Alivn/GAME Lab./CSIE/NDHU
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Previous Works (cont.)
Alivn/GAME Lab./CSIE/NDHU
Exchanging Faces in Images 7
Previous Works (cont.)
Alivn/GAME Lab./CSIE/NDHU
Exchanging Faces in Images 8
Previous Works (cont.)
Alivn/GAME Lab./CSIE/NDHU
Exchanging Faces in Images 9
Previous Works (cont.)
Alivn/GAME Lab./CSIE/NDHU
Exchanging Faces in Images 10
Previous Works (cont.)
Alivn/GAME Lab./CSIE/NDHU
Exchanging Faces in Images 11
OutlineIntroductionMorphable ModelsEstimationExchanging FacesCompositingApplicationResultsConclusions
Alivn/GAME Lab./CSIE/NDHU
Exchanging Faces in Images 12
Morphable Models
A vector space of 3D shapes and textures.Derived from 200 texture Cyberware (TM) laser scans.
100 male and 100 female.In a cylindrical representation with radii r(h, Φ) of surface points
512 equally-spaced angles Φ.512 equally-spaced vertical steps h.
Alivn/GAME Lab./CSIE/NDHU
Exchanging Faces in Images 13
Morphable Models (cont.)
Dense correspondence is computed automatically with an algorithm derived from optical flow.
Alivn/GAME Lab./CSIE/NDHU
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Morphable Models (cont.)
After performing a PCA
m = 149
Alivn/GAME Lab./CSIE/NDHU
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Fitting
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Light Direction And Intensity Estimation
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OutlineIntroductionMorphable ModelsEstimationExchanging FacesCompositingApplicationResultsConclusions
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EstimationAll parameters are estimated simultaneously in an analysis-by-synthesis loop.
All scene parameters are recovered automatically, starting from a frontal pose in the center of the image, and at frontal illumination.
Alivn/GAME Lab./CSIE/NDHU
Exchanging Faces in Images 19
Estimation (cont.)
Cost Function
Alivn/GAME Lab./CSIE/NDHU
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Estimation (cont.)
The optimization is performed with a Stochastic Newton Algorithm.The linear combination of texture Ti cannot reproduce all local characteristics of the novel faces.Extract the texture by an illumination-corrected texture extraction method.
Alivn/GAME Lab./CSIE/NDHU
Exchanging Faces in Images 21
References
A morphable model for the synthesis of 3D faces. SIGGRAPH’99, pp. 187–194.Face recognition based on fitting a 3D morphable model. IEEE Trans. on Pattern Analysis and Machine Intell. 25, 9 (2003), 1063– 1074.
Alivn/GAME Lab./CSIE/NDHU
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OutlineIntroductionMorphable ModelsEstimationExchanging FacesCompositingApplicationResultsConclusions
Alivn/GAME Lab./CSIE/NDHU
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Exchanging FacesBoth 3D shapes are aligned to each other in 3D with 3D Absolute Orientation Algorithm.Both textures have similar illumination.
Illumination-corrected Texture Extraction Algorithm.
Render the face that was reconstructed from the source image with the rendering parameters that were estimated from the target image.
Alivn/GAME Lab./CSIE/NDHU
Exchanging Faces in Images 24
OutlineIntroductionMorphable ModelsEstimationExchanging FacesCompositingApplicationResultsConclusions
Alivn/GAME Lab./CSIE/NDHU
Exchanging Faces in Images 25
Compositing
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Background LayerThe scene of target image, and the original person’s face, hair and body.The novel face may be smaller than the original.
Solved by a background continuation method.
Based on a reflection of pixels beyond the original contour into the face area.
Alivn/GAME Lab./CSIE/NDHU
Exchanging Faces in Images 27
Face LayerThe silhouette of this region:
Occluding contours.Boundaries of hair regions that occlude the skin.Mesh boundaries at the neck and the forehead.
Skin may be partly covered by hair. This hair would be mapped on the face as a texture.
Alivn/GAME Lab./CSIE/NDHU
Exchanging Faces in Images 28
Hair Layer
Drawn in front of the face.Can be used for all faces.Automated classification of pixels into skin and hair is a difficult task.Manually define alpha values for opacity.
Alivn/GAME Lab./CSIE/NDHU
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OutlineIntroductionMorphable ModelsEstimationExchanging FacesCompositingApplicationResultsConclusions
Alivn/GAME Lab./CSIE/NDHU
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ApplicationsCurrent systems are restricted to frontal view of faces.
Alivn/GAME Lab./CSIE/NDHU
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Applications (cont.)
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OutlineIntroductionMorphable ModelsEstimationExchanging FacesCompositingApplicationResultsConclusions
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Exchanging Faces in Images 33
Results
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Results (cont.)
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Results (cont.)
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OutlineIntroductionMorphable ModelsEstimationExchanging FacesCompositingApplicationResultsConclusions
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Exchanging Faces in Images 37
Conclusions
A novel way of processing images on a high level.Only needs simple manual processing steps.For a wide range of applications.Transferring technology from CG to CV.Combines the benefit of image-based method with the versatility of 3D graphics.
Alivn/GAME Lab./CSIE/NDHU
Exchanging Faces in Images 38
Future Works
Fully automated:Detecting facial features.Hair Segmentation.
Exchange faces in video sequences.
Tracking head motion.
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Exchanging Faces in Images 39
Thank you for your patience
Alivn/GAME Lab./CSIE/NDHU
Exchanging Faces in Images 40
Example
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Exchanging Faces in Images 41
Feature Points