Enabling Visual Composition and Animation in Unsupervised Video Generation
Autor: | Davtyan, Aram, Sameni, Sepehr, Ommer, Björn, Favaro, Paolo |
---|---|
Rok vydání: | 2024 |
Předmět: | |
Druh dokumentu: | Working Paper |
Popis: | In this work we propose a novel method for unsupervised controllable video generation. Once trained on a dataset of unannotated videos, at inference our model is capable of both composing scenes of predefined object parts and animating them in a plausible and controlled way. This is achieved by conditioning video generation on a randomly selected subset of local pre-trained self-supervised features during training. We call our model CAGE for visual Composition and Animation for video GEneration. We conduct a series of experiments to demonstrate capabilities of CAGE in various settings. Project website: https://araachie.github.io/cage. Comment: Project website: https://araachie.github.io/cage |
Databáze: | arXiv |
Externí odkaz: |