BlobCtrl: A Unified and Flexible Framework for Element-level Image
  Generation and Editing
Authors: name: Yaowei Li, name: Lingen Li, name: Zhaoyang Zhang, name: Xiaoyu Li, name: Guangzhi Wang, name: Hongxiang Li, name: Xiaodong Cun, name: Ying Shan, name: Yuexian Zou

Element-level visual manipulation is essential in digital content creation,
but current diffusion-based methods lack the precision and flexibility of
traditional tools. In this work, we introduce BlobCtrl, a framework that
unifies element-level generation and editing using a probabilistic blob-based
representation. By employing blobs as visual primitives, our approach
effectively decouples and represents spatial location, semantic content, and
identity information, enabling precise element-level manipulation. Our key
contributions include: 1) a dual-branch diffusion architecture with
hierarchical feature fusion for seamless foreground-background integration; 2)
a self-supervised training paradigm with tailored data augmentation and score
functions; and 3) controllable dropout strategies to balance fidelity and
diversity. To support further research, we introduce BlobData for large-scale
training and BlobBench for systematic evaluation. Experiments show that
BlobCtrl excels in various element-level manipulation tasks while maintaining
computational efficiency, offering a practical solution for precise and
flexible visual content creation. Project page:
https://liyaowei-stu.github.io/project/BlobCtrl/