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.

中文翻译：

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具有 3D 约束和细节增强的人体运动传输

我们提出了一种使用生成对抗网络 (GAN) 进行逼真人体运动转移的新方法，该方法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。我们通过解耦和重新组合源角色和目标角色的姿势信息和外观信息来解决这个问题。我们方法的创新在于使用重建的 3D 人体模型的投影作为 GAN 的条件，以更好地保持不同姿势下传输结果的结构完整性。我们进一步引入了一个细节增强网络，通过利用真实源帧中的细节来增强传输结果的细节。