Deep learning has enabled algorithms to generate realistic images. However,
accurately predicting long video sequences requires understanding long-term
dependencies and remains an open challenge. While existing video prediction
models succeed at generating sharp images, they tend to fail at accurately
predicting far into the future. We introduce the Clockwork VAE (CW-VAE), a
video prediction model that leverages a hierarchy of latent sequences, where
higher levels tick at slower intervals. We demonstrate the benefits of both
hierarchical latents and temporal abstraction on 4 diverse video prediction
datasets with sequences of up to 1000 frames, where CW-VAE outperforms top
video prediction models. Additionally, we propose a Minecraft benchmark for
long-term video prediction. We conduct several experiments to gain insights
into CW-VAE and confirm that slower levels learn to represent objects that
change more slowly in the video, and faster levels learn to represent faster
objects.

介绍了 Clockwork VAE (CW-VAE) 作为视频预测模型，通过利用层次潜变量和时间抽象来处理长期依赖关系，对多个视频预测数据集进行了测试，并在预测远期的效果方面超过了现有的顶尖视频预测模型，同时还提出了一个长期视频预测的 Minecraft 基准。