  Human speech is often accompanied by body gestures including arm and hand
gestures. We present a method that reenacts a high-quality video with gestures
matching a target speech audio. The key idea of our method is to split and
re-assemble clips from a reference video through a novel video motion graph
encoding valid transitions between clips. To seamlessly connect different clips
in the reenactment, we propose a pose-aware video blending network which
synthesizes video frames around the stitched frames between two clips.
Moreover, we developed an audio-based gesture searching algorithm to find the
optimal order of the reenacted frames. Our system generates reenactments that
are consistent with both the audio rhythms and the speech content. We evaluate
our synthesized video quality quantitatively, qualitatively, and with user
studies, demonstrating that our method produces videos of much higher quality
and consistency with the target audio compared to previous work and baselines.
