CAGE: Unsupervised Visual Composition and Animation for Controllable Video Generation

Understand

The field of video generation has expanded significantly in recent years, with controllable and compositional video generation garnering considerable interest.

• Most methods rely on leveraging annotations such as text, objects' bounding boxes, and motion cues, which require substantial human effort and thus limit their scalability.
• In contrast, we address the challenge of controllable and compositional video generation without any annotations by introducing a novel unsupervised approach.
• Our model is trained from scratch on a dataset of unannotated videos.