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

翻译：我们提出了一种基于深度潜变量粒子（DLP）表示的新型以对象为中心的视频预测算法。与现有的基于槽或块的表示相比，DLP使用一组具有学习参数（如位置和大小等属性）的关键点来建模场景，既高效又可解释。我们的方法——深度动态潜变量粒子（DDLP）——在多个具有挑战性的数据集上取得了最先进的以对象为中心的视频预测结果。DDLP的可解释特性使我们能够执行“假设”生成——预测初始帧中对象属性改变后的结果，而DLP的紧凑结构则支持基于扩散模型的高效无条件视频生成。视频、代码和预训练模型可在https://taldatech.github.io/ddlp-web获取。