BlobCtrl
A Unified and Flexible Framework for Element-level Image Generation and Editing
1Peking University, 2ARC Lab, Tencent PCG, 3The Chinese University of Hong Kong
Overview: 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, offering a practical solution for precise and flexible visual content creation.
BlobCtrl Capabilities
(1) Element-level Image Manipulation for precise and flexible visual content creation with fine-grained control over individual elements.
(2) Fidelity & Diversity for generating high-quality and diverse visual content.
(3) Unified Framework for seamless layout and appearance control in both generation and editing tasks.
(1) Blob-based representation: Decouple spatial location, semantic content, and identity information for flexible manipulation.
(2) Dual-branch diffusion model: Seamlessly integrate foreground and background information with hierarchical feature fusion.
(3) Self-supervised training: Tailored data augmentation and score functions with controllable dropout strategies.
(1) BlobData: Large-scale dataset (1.86M samples) with images, masks, ellipse parameters and text descriptions;
(2) BlobBench: Benchmark with 100 curated images for evaluating element-level operations across diverse scenarios;
(3) Evaluation: Framework for assessing identity preservation, grounding accuracy and generation quality.
The dataset curation process involves multiple steps:
• Image Filtering: We filter source images to: (1) Retain images with shorter sides exceeding 480 pixels; (2) Keep only images with valid instance segmentation masks; (3) Apply mask filtering to preserve masks with area ratios between 0.01-0.9 of total image area; (4) Exclude masks touching image boundaries.
• Parameter Extraction: For the filtered masks, we: (1) Fit ellipse parameters using OpenCV's ellipse fitting algorithm; (2) Derive corresponding 2D Gaussian distributions; (3) Remove invalid samples with covariance values below 1e-5.
Annotation: We generate detailed image descriptions using InternVL-2.5, providing rich textual context for each sample in the dataset.
Our evaluation framework assesses multiple aspects:
• Identity Preservation: We evaluate element-level appearance preservation using: (1) CLIP-I scores to measure appearance similarity; (2) DINO scores to assess feature-level preservation between generated and reference images.
• Grounding Accuracy: We evaluate layout control by: (1) Extracting masks from generated images using SAM; (2) Fitting ellipses/bounding boxes to these masks; (3) Computing MSE between fitted annotations and ground truth.
• Quality Metrics: We assess generation and harmonization quality using: (1) FID for distribution similarity; (2) PSNR and SSIM for pixel-level fidelity; (3) LPIPS for perceptual quality.
