exchanging faces in images

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



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.



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



Previous Works



Previous Works (cont.)



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.



Morphable Models (cont.)

Dense correspondence is computed automatically with an algorithm derived from optical flow.



Morphable Models (cont.)

After performing a PCA

m = 149



Fitting



Light Direction And Intensity Estimation



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.



Estimation (cont.)

Cost Function



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.



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.



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.



Compositing



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.



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.



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.



ApplicationsCurrent systems are restricted to frontal view of faces.



Applications (cont.)



Results



Results (cont.)



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.



Future Works

Fully automated:Detecting facial features.Hair Segmentation.

Exchange faces in video sequences.

Tracking head motion.



Thank you for your patience



Example



Feature Points

exchanging faces in images

Documents

d faces

novel faces

imagesfittingexchanging

cost functionexchanging

imagesprevious works

imagesestimation cont

imagesintroduction cont

imagesmorphable models