Res-LGAN for human motion style transfer with more features preserved

Abstract

Human motion style transfer is a technique that aims to apply a desired style to neutral motions, which is an essential aspect of motion generation and retargeting. With the advancement of deep learning networks, significant progress has been made in this field. However, one of the main challenges is preserving the essential features of the original motions, such as velocities and trace, during the style transfer process. To overcome this challenge, we have proposed a novel method called Residual LSTM Generative Adversarial Networks (Res-LGAN) for motion style transfer. The Res-LGAN models consist of a transfer network and a refinement network, which work together to generate smooth and natural stylized motions while preserving key features of the original motions. Additionally, we have introduced a reconstruction loss term to ensure the stylized motions closely retain the features of the original motions. Our experiments demonstrate that the proposed Res-LGAN model outperforms existing state-of-the-art models by generating high-quality stylized motions while preserving the original motion features. To the best of our knowledge, Res-LGAN is the leading method for preserving original content features during motion style transferring.