Predicting future video frames is extremely challenging, as there are many
factors of variation that make up the dynamics of how frames change through
time. Previously proposed solutions require complex inductive biases inside
network architectures with highly specialized computation, including
segmentation masks, optical flow, and foreground and background separation. In
this work, we question if such handcrafted architectures are necessary and
instead propose a different approach: finding minimal inductive bias for video
prediction while maximizing network capacity. We investigate this question by
performing the first large-scale empirical study and demonstrate
state-of-the-art performance by learning large models on three different
datasets: one for modeling object interactions, one for modeling human motion,
and one for modeling car driving.

该研究通过进行大规模的实证研究，针对视频帧预测问题，提出了一种不同于以往手工设计架构的方法：尽可能地降低归纳偏差，同时最大化网络容量，该方法表现良好并在三个不同数据集上进行了验证：建模物体交互、建模人类运动和建模汽车驾驶。