Motion Improvisation: 3D Human Motion Synthesis with a Transformer

ABSTRACT The synthesis of complicated and realistic human motion is a challenging problem and is a significant task for game, film and animation industries. Many existing methods rely on complex and time-consuming keyframe-based methods that demand professional skills in animation software and motion capture hardware. On the other hand, casual users seek a playful experience to animate their favorite characters with a simple and easy-to-use tool. Recent work has explored building intuitive animation systems but suffers from inability to generate complex and expressive motions. To tackle this limitation, we present a keyframe-driven animation synthesis algorithm that is able to produce complex human motions with a few input keyframes, allowing the user to control the keyframes at will. Inspired by the success of attention-based techniques in natural language processing, our method completes body motions in a sequence-to-sequence manner and captures motion dependencies both spatially and temporally. We evaluate our method qualitatively and quantitatively on the LaFAN1 dataset, demonstrating improved accuracy compared with state of the art methods.