GAC-GAN: A General Method for Appearance-Controllable Human Video Motion Transfer

Human video motion transfer has a wide range of applications in multimedia, computer vision, and graphics. Recently, due to the rapid development of Generative Adversarial Networks (GANs), there has been significant progress in the field. However, almost all existing GAN-based works are prone to address the mapping from human motions to video scenes, with scene appearances encoded individually in the trained models. Therefore, each trained model can only generate videos with a specific scene appearance, and new models are required to be trained to generate new appearances. Besides, existing works lack the capability of appearance control. For example, users have to provide video records of wearing new clothes or performing in new backgrounds to enable clothes or background changing in their synthetic videos, which greatly limits the application flexibility. In this paper, we propose General Appearance-Controllable GAN (GAC-GAN), a general method for appearance-controllable human video motion transfer. To enable general-purpose appearance synthesis, we propose to include appearance information in the conditioning inputs. Thus, once trained, our model can generate new appearances by altering the input appearance information. To achieve appearance control, we first obtain the appearance-controllable conditioning inputs, and then utilize a two-stage GAC-GAN to generate the corresponding appearance-controllable outputs, where we utilize an Appearance-Consistency GAN (ACGAN) loss, and a shadow extraction module for output foreground, and background appearance control respectively. We further build a solo dance dataset containing a large number of dance videos for training, and evaluation. Experimental results on our solo dance dataset, and iPER dataset show that our proposed GAC-GAN can not only support appearance-controllable human video motion transfer but also achieve higher video quality than state-of-art methods.