BPA-GAN: Human Motion Transfer Using Body-Part-Aware Generative Adversarial Networks

Abstract Human motion transfer has many applications in human behavior analysis, training data augmentation, and personalization in mixed reality. We propose a Body-Parts-Aware Generative Adversarial Network (BPA-GAN) for image-based human motion transfer. Our key idea is to take advantage of the human body with segmented parts instead of using the human skeleton like most of existing methods to encode the human motion information. As a result, we improve the reconstruction quality, the training efficiency, and the temporal consistency via training multiple GANs in a local-to-global manner and adding regularization on the source motion. Extensive experiments show that our method outperforms the baseline and the state-of-the-art techniques in preserving the details of body parts.