Current deep learning results on video generation are limited while there are only a few first results on video prediction and no relevant significant results on video completion. This is due to the severe ill-posedness inherent in these three problems. In this paper, we focus on human action videos, and propose a general, two-stage deep framework to generate human action videos with no constraints or arbitrary number of constraints, which uniformly address the three problems: video generation given no input frames, video prediction given the first few frames, and video completion given the first and last frames. To make the problem tractable, in the first stage we train a deep generative model that generates a human pose sequence from random noise. In the second stage, a skeleton-to-image network is trained, which is used to generate a human action video given the complete human pose sequence generated in the first stage. By introducing the two-stage strategy, we sidestep the original ill-posed problems while producing for the first time high-quality video generation/prediction/completion results of much longer duration. We present quantitative and qualitative evaluation to show that our two-stage approach outperforms state-of-the-art methods in video generation, prediction and video completion. Our video result demonstration can be viewed at https://iamacewhite.github.io/supp/index.html.

在这篇论文中，我们提出了一个通用的两阶段深度框架，以生成人体动作视频并统一解决了视频生成，视频预测和视频完成等三种问题。我们在第一阶段训练了一个深度生成模型，用于从随机噪声生成人体姿势序列。在第二阶段，我们训练了一个骨架图像网络，用于生成完整的人体动作视频。通过引入两阶段策略，我们成功地解决了原始的难解问题，并在视频生成、预测和完成方面取得了最先进的结果。

深度视频生成，预测和完成人类动作序列