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

QS-Craft: Learning to Quantize, Scrabble and Craft for Conditional Human Motion Animation