Motion, scene and object are three primary visual components of a video. In
particular, objects represent the foreground, scenes represent the background,
and motion traces their dynamics. Based on this insight, we propose a two-stage
MOtion, Scene and Object decomposition framework (MOSO) for video prediction,
consisting of MOSO-VQVAE and MOSO-Transformer. In the first stage, MOSO-VQVAE
decomposes a previous video clip into the motion, scene and object components,
and represents them as distinct groups of discrete tokens. Then, in the second
stage, MOSO-Transformer predicts the object and scene tokens of the subsequent
video clip based on the previous tokens and adds dynamic motion at the token
level to the generated object and scene tokens. Our framework can be easily
extended to unconditional video generation and video frame interpolation tasks.
Experimental results demonstrate that our method achieves new state-of-the-art
performance on five challenging benchmarks for video prediction and
unconditional video generation: BAIR, RoboNet, KTH, KITTI and UCF101. In
addition, MOSO can produce realistic videos by combining objects and scenes
from different videos.

该研究提出了一种基于 MOtion，Scene 和 Object 分解的框架（MOSO）用于视频预测，并在多个基准测试中取得了最先进的性能表现。