  We propose a new method for realistic human motion transfer using a
generative adversarial network (GAN), which generates a motion video of a
target character imitating actions of a source character, while maintaining
high authenticity of the generated results. We tackle the problem by decoupling
and recombining the posture information and appearance information of both the
source and target characters. The innovation of our approach lies in the use of
the projection of a reconstructed 3D human model as the condition of GAN to
better maintain the structural integrity of transfer results in different
poses. We further introduce a detail enhancement net to enhance the details of
transfer results by exploiting the details in real source frames. Extensive
experiments show that our approach yields better results both qualitatively and
quantitatively than the state-of-the-art methods.
