A Multi-Task Motion Generation Model that Fuses a Discriminator and a Generator

The human motion generation model can extract structural features from existing human motion capture data, and the generated data makes animated characters move. The 3D human motion capture sequences contain complex spatial-temporal structures, and the deep learning model can fully describe the potential semantic structure of human motion. To improve the authenticity of the generated human motion sequences, we propose a multi-task motion generation model that consists of a discriminator and a generator. The discriminator classifies motion sequences into different styles according to their similarity to the mean spatial-temporal templates from motion sequences of 17 crucial human joints in three-freedom degrees. And target motion sequences are created with these styles by the generator. Unlike traditional related works, our model can handle multiple tasks, such as identifying styles and generating data. In addition, by extracting 17 crucial joints from 29 human joints, our model avoids data redundancy and improves the accuracy of model recognition. The experimental results show that the discriminator of the model can effectively recognize diversified movements, and the generated data can correctly fit the actual data. The combination of discriminator and generator solves the problem of low reuse rate of motion data, and the generated motion sequences are more suitable for actual movement.