Unsupervised video-based object-centric learning is a promising avenue to learn structured representations from large, unlabeled video collections, but previous approaches have only managed to scale to real-world datasets in restricted domains.Recently, it was shown that the reconstruction of pre-trained self-supervised features leads to object-centric representations on unconstrained real-world image datasets.Building on this approach, we propose a novel way to use such pre-trained features in the form of a temporal feature similarity loss.This loss encodes semantic and temporal correlations between image patches and is a natural way to introduce a motion bias for object discovery.We demonstrate that this loss leads to state-of-the-art performance on the challenging synthetic MOVi datasets.When used in combination with the feature reconstruction loss, our model is the first object-centric video model that scales to unconstrained video datasets such as YouTube-VIS.https://martius-lab.github.io/videosaur/