BlobCtrl: A Unified and Flexible Framework for Element-level Image Generation and Editing

2025-03-17

AI Summary

Existing diffusion models struggle to simultaneously achieve precision and flexibility in element-level image generation and editing. To address this, BlobCtrl introduces a probabilistic blob representation paradigm that disentangles an element’s position, semantics, and identity, enabling fine-grained, controllable manipulation. Methodologically, it employs a dual-branch hierarchical fusion diffusion architecture, integrating self-supervised training via a customized score function and a controllable dropout mechanism to dynamically balance fidelity and diversity. Technically, it unifies blob-based representation, hierarchical feature fusion, and element-aware data augmentation. Evaluated on the newly constructed benchmark BlobBench, BlobCtrl achieves state-of-the-art performance with high computational efficiency. Trained and validated on the large-scale blob-centric dataset BlobData, the model significantly improves controllability, consistency, and generalization in element-level editing.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Representation Learning
• Intelligent Robots: Manipulation

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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/

Problem

• Enables precise element-level image generation and editing
• Unifies spatial, semantic, and identity information using blobs
• Improves flexibility and precision over current diffusion-based methods

Innovation

• Probabilistic blob-based representation for precise manipulation
• Dual-branch diffusion architecture with hierarchical feature fusion
• Self-supervised training with tailored data augmentation