Hierarchical learning recurrent neural networks for 3D motion synthesis

Three-dimensional human motion synthesis is one of the key technologies in the field of computer animation and multimedia applications. It is well known that the human body's own motion is full of strong personality, emotion, and high-dimensional characteristics, leading to the automatic synthesis of diverse and lifelike 3D human motion data continues to be a challenging task. Facing the challenge, this paper proposes a human motion synthesis framework based on hierarchical learning recurrent neural networks (HL-RNN). The framework includes a low-level network and a high-level network, which are used to extract the path information of the movement and the spatio-temporal relationship of the human bone structure, respectively. Then, after fusion, motions that satisfy the path constraints could be generated. This method can not only synthesize high-quality human movements that follow a specified trajectory, but also synthesize smooth transitions between various movements, and can also be used to synthesize data of different motion styles. Compared with some latest methods, experiments showed that the proposed method can significantly improve the quality and generalization performance of motion synthesis.