A paper from our group got accepted to AAAI 2025!
Aram Davtyan, Sepehr Sameni, Björn Ommer, Paolo Favaro, in AAAI Conference on Artificial Intelligence, 2025
The field of video generation has expanded significantly in recent years, with controllable and compositional video generation garnering considerable interest. Traditionally, achieving this has relied on leveraging annotations such as text, objects' bounding boxes, and motion cues, which require substantial human effort and thus limit its scalability. Thus, we address the challenge of controllable and compositional video generation without any annotations by introducing a novel unsupervised approach. Once trained from scratch on a dataset of unannotated videos, our model can effectively compose scenes by assembling predefined object parts and animating them in a plausible and controlled manner. The core innovation of our method lies in its training process, where video generation is conditioned on a randomly selected subset of pre-trained self-supervised local features. This conditioning compels the model to learn how to inpaint the missing information in the video both spatially and temporally, thereby resulting in understanding the inherent compositionality and the dynamics of the scene. The abstraction level and the imposed invariance of the conditioning to minor visual perturbations enable control over object motion by simply moving the features to the desired future locations. We call our model CAGE, which stands for visual Composition and Animation for video GEneration. We conduct extensive experiments to validate the effectiveness of CAGE across various scenarios, demonstrating its capability to accurately follow the control and to generate high-quality videos that exhibit coherent scene composition and realistic animation.
Project website: https://araachie.github.io/cage.
Paper: https://arxiv.org/abs/2403.14368