We propose a conditional generative adversarial network (GAN) model for zero-shot video generation. In this study, we have explored zero-shot conditional generation setting. In other words, we generate unseen videos from training samples with missing classes. The task is an extension of conditional data generation. The key idea is to learn disentangled representations in the latent space of a GAN. To realize this objective, we base our model on the motion and content decomposed GAN and conditional GAN for image generation. We build the model to find better-disentangled representations and to generate good-quality videos. We demonstrate the effectiveness of our proposed model through experiments on the Weizmann action database and the MUG facial expression database.

翻译：我们为零光视频生成建议一个有条件的基因对抗网络模式。 我们在本研究中探讨了零光有条件生成设置。 换句话说, 我们从缺少课程的培训样本中生成了隐蔽的视频。 任务就是扩展有条件数据生成。 关键的想法是学习GAN潜在空间的分解表达方式。 为了实现这一目标, 我们以分解的GAN运动和内容以及图像生成的有条件GAN为模型基础。 我们构建了模型, 以找到更加分解的表达方式, 并生成高质量的视频。 我们通过 Weizmann行动数据库和MUG面部表达方式数据库的实验, 展示了我们提议的模型的有效性 。