From CNN, RNN, to ViT, we have witnessed remarkable advancements in video
prediction, incorporating auxiliary inputs, elaborate neural architectures, and
sophisticated training strategies. We admire these progresses but are confused
about the necessity: is there a simple method that can perform comparably well?
This paper proposes SimVP, a simple video prediction model that is completely
built upon CNN and trained by MSE loss in an end-to-end fashion. Without
introducing any additional tricks and complicated strategies, we can achieve
state-of-the-art performance on five benchmark datasets. Through extended
experiments, we demonstrate that SimVP has strong generalization and
extensibility on real-world datasets. The significant reduction of training
cost makes it easier to scale to complex scenarios. We believe SimVP can serve
as a solid baseline to stimulate the further development of video prediction.
The code is available at
\href{this https URL}{Github}.

本文介绍了一个被称为 SimVP 的简单的视频预测模型，使用 CNN 构建，仅仅通过 MSE 损失进行端到端的训练，取得了在五个基准数据集上的最新成果，同时通过扩展实验，证明 SimVP 在现实世界数据集上具有强大的泛化能力和可扩展性，大大降低了训练成本。