We present GLASS, a method for Global and Local Action-driven Sequence
Synthesis. GLASS is a generative model that is trained on video sequences in an
unsupervised manner and that can animate an input image at test time. The
method learns to segment frames into foreground-background layers and to
generate transitions of the foregrounds over time through a global and local
action representation. Global actions are explicitly related to 2D shifts,
while local actions are instead related to (both geometric and photometric)
local deformations. GLASS uses a recurrent neural network to transition between
frames and is trained through a reconstruction loss. We also introduce
W-Sprites (Walking Sprites), a novel synthetic dataset with a predefined action
space. We evaluate our method on both W-Sprites and real datasets, and find
that GLASS is able to generate realistic video sequences from a single input
image and to successfully learn a more advanced action space than in prior
work.

GLASS 是一种用于全局和局部序列合成的生成模型，它能够在测试时间内使输入图像动画化，并通过递归神经网络来在视频帧之间进行过渡，透过全局和局部行动表示来学习分割前景 - 背景层和生成前景物体随时间移动的方法，并通过自编码器损失进行训练。我们还介绍了全新的合成数据集 W-Sprites，评估了我们的方法在 W-Sprites 和真实数据集上的性能，发现 GLASS 能够从单个输入图像生成逼真的视频序列，并成功地学习了比之前更高级的行动空间。