We propose a new method for realistic human motion transfer using a generative adversarial network (GAN), which generates a motion video of a target character imitating actions of a source character, while maintaining high authenticity of the generated results. We tackle the problem by decoupling and recombining the posture information and appearance information of both the source and target characters. The innovation of our approach lies in the use of the projection of a reconstructed 3D human model as the condition of GAN to better maintain the structural integrity of transfer results in different poses. We further introduce a detail enhancement net to enhance the details of transfer results by exploiting the details in real source frames. Extensive experiments show that our approach yields better results both qualitatively and quantitatively than the state-of-the-art methods.

翻译：我们提出了一种基于生成对抗网络(GAN)的真实人体动作迁移新方法，该方法可生成目标角色模仿源角色动作的运动视频，同时保持生成结果的高度真实性。我们将问题分解为对源角色和目标角色的姿态信息与外观信息进行解耦与重组。本方法的创新之处在于：将重建的三维人体模型的投影作为GAN的条件输入，以更好地保持不同姿态下迁移结果的结构完整性。我们进一步引入细节增强网络，通过利用真实源帧中的细节信息来增强迁移结果的细节表现。大量实验表明，本方法在定性和定量评估中均取得了优于现有最优方法的结果。