  Object-centric learning aims to represent visual data with a set of object
entities (a.k.a. slots), providing structured representations that enable
systematic generalization. Leveraging advanced architectures like Transformers,
recent approaches have made significant progress in unsupervised object
discovery. In addition, slot-based representations hold great potential for
generative modeling, such as controllable image generation and object
manipulation in image editing. However, current slot-based methods often
produce blurry images and distorted objects, exhibiting poor generative
modeling capabilities. In this paper, we focus on improving slot-to-image
decoding, a crucial aspect for high-quality visual generation. We introduce
SlotDiffusion -- an object-centric Latent Diffusion Model (LDM) designed for
both image and video data. Thanks to the powerful modeling capacity of LDMs,
SlotDiffusion surpasses previous slot models in unsupervised object
segmentation and visual generation across six datasets. Furthermore, our
learned object features can be utilized by existing object-centric dynamics
models, improving video prediction quality and downstream temporal reasoning
tasks. Finally, we demonstrate the scalability of SlotDiffusion to
unconstrained real-world datasets such as PASCAL VOC and COCO, when integrated
with self-supervised pre-trained image encoders.
