  We present a new application direction named Pareidolia Face Reenactment,
which is defined as animating a static illusory face to move in tandem with a
human face in the video. For the large differences between pareidolia face
reenactment and traditional human face reenactment, two main challenges are
introduced, i.e., shape variance and texture variance. In this work, we propose
a novel Parametric Unsupervised Reenactment Algorithm to tackle these two
challenges. Specifically, we propose to decompose the reenactment into three
catenate processes: shape modeling, motion transfer and texture synthesis. With
the decomposition, we introduce three crucial components, i.e., Parametric
Shape Modeling, Expansionary Motion Transfer and Unsupervised Texture
Synthesizer, to overcome the problems brought by the remarkably variances on
pareidolia faces. Extensive experiments show the superior performance of our
method both qualitatively and quantitatively. Code, model and data are
available on our project page.
