Title: Learning Object-Centric Dynamic Modes from Video and Emerging Properties. Abstract: One of the long-term objectives of Artificial Intelligence  is to endow machines with the capacity of structuring and interpreting the world as we do. Towards this goal, recent methods have successfully  decomposed and disentangled video sequences into their composing objects, attributes and dynamics, in a self-supervised fashion. However, there have been scarce efforts to propose useful decompositions of the dynamics in a scene. We propose a method to decompose a video into moving objects, their attributes and the dynamic modes of their trajectories. We model the objects' dynamics with linear system identification tools, by means of a Koopman mapping and the Koopman operator $\mathcal{K}$. This allows user access and interpretation of the dynamics in the scene. We test our framework in a variety of datasets, while illustrating the novel features that emerge from our dynamic modes decomposition: temporal super-resolution, backwards forecasting, model reduction and video dynamics interpretation and manipulation at test-time. We successfully forecast challenging object trajectories from pixels, achieving competitive performance while drawing useful insights.