QS-Craft: Learning to Quantize, Scrabble and Craft for Conditional Human Motion Animation
نویسندگان
چکیده
AbstractThis paper studies the task of conditional Human Motion Animation (cHMA). Given a source image and driving video, model should animate new frame sequence, in which person perform similar motion as pose sequence from video. Despite success Generative Adversarial Network (GANs) methods video synthesis, it is still very challenging to conduct cHMA due difficulty efficiently utilizing guided information such images or poses, generating good visual quality. To this end, proposes novel learning Quantize, Scrabble, Craft (QS-Craft) for human animation. The key novelties come newly introduced three steps: quantize, scrabble craft. Particularly, our QS-Craft employs transformer its structure utilize attention architectures. represented coordinate extracted videos. Extensive experiments on datasets validate efficacy model.
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ژورنال
عنوان ژورنال: Lecture Notes in Computer Science
سال: 2023
ISSN: ['1611-3349', '0302-9743']
DOI: https://doi.org/10.1007/978-3-031-26351-4_27