论文标题
人类运动转移具有3D约束和细节增强
Human Motion Transfer with 3D Constraints and Detail Enhancement
论文作者
论文摘要
我们提出了一种使用生成对抗网络（GAN）的新方法，用于现实的人类运动转移，该方法生成了模仿源角色的目标角色动作的运动视频，同时保持生成结果的高真实性。我们通过将源和目标角色的姿势信息和外观信息解耦和重组来解决问题。我们方法的创新在于使用重建的3D人类模型的投影作为GAN的条件，以更好地维持转移的结构完整性，以不同的姿势导致。我们进一步介绍了一个细节增强网，以通过利用实际源框架中的详细信息来增强转移结果的细节。广泛的实验表明，与最先进的方法相比，我们的方法在定性和定量上产生更好的结果。
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.
