Creating realistic human videos entails the challenge of being able to
simultaneously generate both appearance, as well as motion. To tackle this
challenge, we introduce G$^{3}$AN, a novel spatio-temporal generative model,
which seeks to capture the distribution of high dimensional video data and to
model appearance and motion in disentangled manner. The latter is achieved by
decomposing appearance and motion in a three-stream Generator, where the main
stream aims to model spatio-temporal consistency, whereas the two auxiliary
streams augment the main stream with multi-scale appearance and motion
features, respectively. An extensive quantitative and qualitative analysis
shows that our model systematically and significantly outperforms
state-of-the-art methods on the facial expression datasets MUG and UvA-NEMO, as
well as the Weizmann and UCF101 datasets on human action. Additional analysis
on the learned latent representations confirms the successful decomposition of
appearance and motion. Source code and pre-trained models are publicly
available.

提出了一种名为 G$^{3}$AN 的新型时空生成模型，用于捕捉高维视频数据的分布，并以分离的方式模拟外观和动作，在面部表情数据集 MUG 和 UvA-NEMO，以及人类行动数据集 Weizmann 和 UCF101 上显著优于现有方法，并分析了学习到的潜在表示的成功分解。