Video prediction aims to generate realistic future frames by learning dynamic
visual patterns. One fundamental challenge is to deal with future uncertainty:
How should a model behave when there are multiple correct, equally probable
future? We propose an Appearance-Motion Conditional GAN to address this
challenge. We provide appearance and motion information as conditions that
specify how the future may look like, reducing the level of uncertainty. Our
model consists of a generator, two discriminators taking charge of appearance
and motion pathways, and a perceptual ranking module that encourages videos of
similar conditions to look similar. To train our model, we develop a novel
conditioning scheme that consists of different combinations of appearance and
motion conditions. We evaluate our model using facial expression and human
action datasets and report favorable results compared to existing methods.

该研究介绍了一种通过给定外观和运动条件生成未来图像的方法，在条件不确定性下有着很好的表现，通过开发创新的条件方案，结合对外观和动作的识别，能够更好地进行视频预测，已在面部表情和人类动作数据集上进行了验证。