We identify two pathological cases of temporal inconsistencies in video
generation: video freezing and video looping. To better quantify the temporal
diversity, we propose a class of complementary metrics that are effective, easy
to implement, data agnostic, and interpretable. Further, we observe that
current state-of-the-art models are trained on video samples of fixed length
thereby inhibiting long-term modeling. To address this, we reformulate the
problem of video generation as a Markov Decision Process (MDP). The underlying
idea is to represent motion as a stochastic process with an infinite forecast
horizon to overcome the fixed length limitation and to mitigate the presence of
temporal artifacts. We show that our formulation is easy to integrate into the
state-of-the-art MoCoGAN framework. Our experiments on the Human Actions and
UCF-101 datasets demonstrate that our MDP-based model is more memory efficient
and improves the video quality both in terms of the new and established
metrics.

本研究提出基于 MDP 的视频生成问题解决方案，用于克服视频长度不可控和时间架构缺陷问题，结合 MoCoGAN 框架实现。实验结果表明该方案能够有效提高视频生成的质量。