Tokenizer, serving as a translator to map the intricate visual data into a compact latent space, lies at the core of visual generative models.

Transformer [70]has brought significant progress in natural language processing [17,7,54]. The vision transformer [20] also improves a series of computer vision tasks including image classification [66,88], object detection [8,37], semantic segmentation [80], object tracking [13,16], and video recognition [6,3].

A large part of the current success of deep learning lies in the effectiveness of data - more precisely: labelled data. Yet, labelling a dataset with human annotation continues to carry high costs, especially for videos. While in the image domain, recent methods have allowed to generate meaningful (pseudo-) labels for unlabelled datasets without supervision, this development is missing for the video domain where learning feature representations is the current focus.