  Sharing short personalized videos to various social media networks has become
quite popular in recent years. This raises the need for digital retouching of
portraits in videos. However, applying portrait image editing directly on
portrait video frames cannot generate smooth and stable video sequences. To
this end, we present a robust and easy-to-use parametric method to reshape the
portrait in a video to produce smooth retouched results. Given an input
portrait video, our method consists of two main stages: stabilized face
reconstruction, and continuous video reshaping. In the first stage, we start by
estimating face rigid pose transformations across video frames. Then we jointly
optimize multiple frames to reconstruct an accurate face identity, followed by
recovering face expressions over the entire video. In the second stage, we
first reshape the reconstructed 3D face using a parametric reshaping model
reflecting the weight change of the face, and then utilize the reshaped 3D face
to guide the warping of video frames. We develop a novel signed distance
function based dense mapping method for the warping between face contours
before and after reshaping, resulting in stable warped video frames with
minimum distortions. In addition, we use the 3D structure of the face to
correct the dense mapping to achieve temporal consistency. We generate the
final result by minimizing the background distortion through optimizing a
content-aware warping mesh. Extensive experiments show that our method is able
to create visually pleasing results by adjusting a simple reshaping parameter,
which facilitates portrait video editing for social media and visual effects.
