  Most methods for conditional video synthesis use a single modality as the
condition. This comes with major limitations. For example, it is problematic
for a model conditioned on an image to generate a specific motion trajectory
desired by the user since there is no means to provide motion information.
Conversely, language information can describe the desired motion, while not
precisely defining the content of the video. This work presents a multimodal
video generation framework that benefits from text and images provided jointly
or separately. We leverage the recent progress in quantized representations for
videos and apply a bidirectional transformer with multiple modalities as inputs
to predict a discrete video representation. To improve video quality and
consistency, we propose a new video token trained with self-learning and an
improved mask-prediction algorithm for sampling video tokens. We introduce text
augmentation to improve the robustness of the textual representation and
diversity of generated videos. Our framework can incorporate various visual
modalities, such as segmentation masks, drawings, and partially occluded
images. It can generate much longer sequences than the one used for training.
In addition, our model can extract visual information as suggested by the text
prompt, e.g., "an object in image one is moving northeast", and generate
corresponding videos. We run evaluations on three public datasets and a newly
collected dataset labeled with facial attributes, achieving state-of-the-art
generation results on all four.
