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1. Facial Expression Editing in Video Using a Temporally- Smooth Factorization 2. Face Swapping: Automatically Replacing Faces in Photographs.

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Presentation on theme: "1. Facial Expression Editing in Video Using a Temporally- Smooth Factorization 2. Face Swapping: Automatically Replacing Faces in Photographs."— Presentation transcript:

1 1. Facial Expression Editing in Video Using a Temporally- Smooth Factorization 2. Face Swapping: Automatically Replacing Faces in Photographs

2 Facial Expression Editing in Video Using a Temporally-Smooth Factorization Fei Yang, Lubomir Bourdev, Eli Shechtman, Jue Wang, Dimitris Metaxas CVPR 2012

3 Goal The goal is to allow for semantic-level editing of expressions in a video: magnifying an expression suppressing an expression replacing by another expressions 3

4 Example 4

5 Challenges Natural expression Different parts changes accordingly Unique identity Temporal coherency 5

6 Related Work 2D based methods [Theobald09], [Liu01], [Williams90], … 3D based methods [Blanz03], [Pighin98], … Expression flow [Yang11]… Frame reorder method [Bregler98], [Kemelmacher- Shlizerman11] Tensor factorization methods [Vlasic05], [Dale11]… 6

7 Algorithm 7 Expression Information Identity Information 3D Tensor Model - [Vlasic et al siggraph05] Modify

8 Mode-n Product 8

9 Algorithm goal to identify a and method 2D v.s. 3D frame t Minimize: | – | = Weak Projective Matrix R t

10 Algorithm Fitting Error: Shape Distribution Constraint: Temporal coherence:

11 Algorithm 11 Levenberg-Marquardt (Siggraph98)

12 Algorithm 12 Adjust to achieve expression modification  Dynamic Time Warping (DTW) [Sakoe78]  Residual Expression Flow  Correcting boundary compatibility

13 Results 13

14 Face Swapping: Automatically Replacing Faces in Photographs Dmitri Bitouk Neeraj Kumar Samreen Dhillon Peter Belhumeur Shree K. Nayar Siggraph 2008

15 Examples 15

16 Goals 16 For an input image: Automatically find the best candidate Automatically replace the face Automatically color and lighting adjustmet

17 Library Building 17 OKAO face detector to detect face pose [Omron07]

18 Process 18

19 Alignment 19 Pose, Resolution, and Image Blur: Yaw, pitch threshold between two images ( ) Eye distance as a measure of distance (80%) Similarity of the blur degrees [Kundur and Hatzinakos 1996; Fergus et al. 2006]

20 Color and Lighting 20 To ensure the similarity between the replaced and original face, a linear combination of 9 spherical harmonics [Ramamoorthi and Hanrahan 2001; Basri and Jacobs 2003] is used as measure metric: Each pixel I(x, y) can be approximated by: Distance:

21 Seam Signature by-256 patch from the face is used for replacement. Unfold: L2 Norm is used to compute the distance

22 Appearance Adjustment Using simple scaling on the Harmonics coefficients, are the original and replacement images Scale the replaced image

23 Results

24 The End 24 Any Questions ?


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