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Video Face Replacement Kevin Dale1 1 Kalyan Sunkavalli1 Harvard University a Source b Target Micah K Johnson2 2 MIT CSAIL c Aligned Daniel Vlasic3 3 Wojciech Matusik2 4 Lantos Technologies 4 Hanspeter Pfister1 Disney Research Zurich d Three frames of the blended result Figure 1 Our method for face replacement requires only single camera video of the source a and target b subject which allows for simple acquisition and reuse of existing footage We track both performances with a multilinear morphable model then spatially and temporally align the source face to the target footage c We then compute an optimal seam for gradient domain compositing that minimizes bleeding and flickering in the final result d Abstract We present a method for replacing facial performances in video Our approach accounts for differences in identity visual appearance speech and timing between source and target videos Unlike prior work it does not require substantial manual operation or complex acquisition hardware only single camera video We use a 3D multilinear model to track the facial performance in both videos Using the corresponding 3D geometry we warp the source to the target face and retime the source to match the target performance We then compute an optimal seam through the video volume that maintains temporal consistency in the final composite We showcase the use of our method on a variety of examples and present the result of a user study that suggests our results are difficult to distinguish from real video footage CR Categories I 4 3 Image Processing and Computer Vision Enhancement Filtering I 3 7 Computer Graphics ThreeDimensional Graphics and Realism Animation Keywords face replacement facial animation video compositing Links 1 DL PDF Introduction Techniques for manipulating and replacing faces in photographs have matured to the point that realistic results can be obtained with minimal user input e g Agarwala et al 2004 Bitouk et al 2008 Sunkavalli et al 2010 Face replacement in



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