  This paper studies the task of conditional Human Motion Animation (cHMA).
Given a source image and a driving video, the model should animate the new
frame sequence, in which the person in the source image should perform a
similar motion as the pose sequence from the driving video. Despite the success
of Generative Adversarial Network (GANs) methods in image and video synthesis,
it is still very challenging to conduct cHMA due to the difficulty in
efficiently utilizing the conditional guided information such as images or
poses, and generating images of good visual quality. To this end, this paper
proposes a novel model of learning to Quantize, Scrabble, and Craft (QS-Craft)
for conditional human motion animation. The key novelties come from the newly
introduced three key steps: quantize, scrabble and craft. Particularly, our
QS-Craft employs transformer in its structure to utilize the attention
architectures. The guided information is represented as a pose coordinate
sequence extracted from the driving videos. Extensive experiments on human
motion datasets validate the efficacy of our model.

Computer Vision and Pattern Recognition