  We propose a new object-centric video prediction algorithm based on the deep
latent particle (DLP) representation. In comparison to existing slot- or
patch-based representations, DLPs model the scene using a set of keypoints with
learned parameters for properties such as position and size, and are both
efficient and interpretable. Our method, deep dynamic latent particles (DDLP),
yields state-of-the-art object-centric video prediction results on several
challenging datasets. The interpretable nature of DDLP allows us to perform
``what-if'' generation -- predict the consequence of changing properties of
objects in the initial frames, and DLP's compact structure enables efficient
diffusion-based unconditional video generation. Videos, code and pre-trained
models are available: https://taldatech.github.io/ddlp-web
