We present a video generation model that accurately reproduces object motion,
changes in camera viewpoint, and new content that arises over time. Existing
video generation methods often fail to produce new content as a function of
time while maintaining consistencies expected in real environments, such as
plausible dynamics and object persistence. A common failure case is for content
to never change due to over-reliance on inductive biases to provide temporal
consistency, such as a single latent code that dictates content for the entire
video. On the other extreme, without long-term consistency, generated videos
may morph unrealistically between different scenes. To address these
limitations, we prioritize the time axis by redesigning the temporal latent
representation and learning long-term consistency from data by training on
longer videos. To this end, we leverage a two-phase training strategy, where we
separately train using longer videos at a low resolution and shorter videos at
a high resolution. To evaluate the capabilities of our model, we introduce two
new benchmark datasets with explicit focus on long-term temporal dynamics.

本研究提出了一种视频生成模型，通过重新设计时域隐变量表示和学习长期一致性并进行长时间分辨率分别为低和高的两阶段训练策略来优化生成视频的时序一致性，所得的模型经过新的基准数据集的测试，取得了良好的效果。