  Existing person video generation methods either lack the flexibility in
controlling both the appearance and motion, or fail to preserve detailed
appearance and temporal consistency. In this paper, we tackle the problem of
motion transfer for generating person videos, which provides controls on both
the appearance and the motion. Specifically, we transfer the motion of one
person in a target video to another person in a source video, while preserving
the appearance of the source person. Besides only relying on one source frame
as the existing state-of-the-art methods, our proposed method integrates
information from multiple source frames based on a spatio-temporal attention
mechanism to preserve rich appearance details. In addition to a spatial
discriminator employed for encouraging the frame-level fidelity, a multi-range
temporal discriminator is adopted to enforce the generated video to resemble
temporal dynamics of a real video in various time ranges. A challenging
real-world dataset, which contains about 500 dancing video clips with complex
and unpredictable motions, is collected for the training and testing. Extensive
experiments show that the proposed method can produce more photo-realistic and
temporally consistent person videos than previous methods. As our method
decomposes the syntheses of the foreground and background into two branches, a
flexible background substitution application can also be achieved.
