Predicting future frames in natural video sequences is a new challenge that is receiving increasing attention in the computer vision community. However, existing models suffer from severe loss of temporal information when the predicted sequence is long. Compared to previous methods focusing on generating more realistic contents, this paper extensively studies the importance of sequential order information for video generation. A novel Shuffling sEquence gEneration network (SEE-Net) is proposed that can learn to discriminate unnatural sequential orders by shuffling the video frames and comparing them to the real video sequence. Systematic experiments on three datasets with both synthetic and real-world videos manifest the effectiveness of shuffling sequence generation for video prediction in our proposed model and demonstrate state-of-the-art performance by both qualitative and quantitative evaluations. The source code is available at https://github.com/andrewjywang/SEENet.

本研究提出一种名为Shuffling sEquence gEneration Network (SEE-Net) 的模型，通过打乱视频帧的顺序并将其与真实视频序列进行比较来学习区分非自然的顺序信息，从而提升了视频预测的效果，实验结果表明该模型在合成和实际场景视频上的表现都优于其他先前的方法，其具有生成更加真实的内容，顺序信息对于视频生成至关重要的特点。

顺序有关：针对视频预测的乱序序列生成