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This paper introduces CSGen, a hierarchical multimodal diffusion model designed to generate high-fidelity images of curvilinear structures while adhering to multiple control conditions. By leveraging a comprehensive dataset of over 24,000 samples and employing a novel hierarchical progressive control strategy, CSGen effectively mitigates semantic drift and preserves topological integrity. The model's performance is validated through extensive experiments, demonstrating superior structure accuracy and visual realism, which significantly enhances downstream segmentation tasks across various prompts.
CSGen achieves unprecedented accuracy in generating complex curvilinear structures, outperforming existing methods in both visual fidelity and segmentation performance.
Curvilinear structure analysis is an important and fundamental task in multimedia. However, the controllable generation of images with precise curvilinear structure objects remains an open challenge. To address this, we propose CSGen, a hierarchical multimodal diffusion model that synthesizes high-fidelity images precisely aligned with multiple control conditions. The CSGen is built upon three key innovations: 1) We construct a multi-domain and multimodal dataset, including over 24K samples from 5 domains and 7 different types of annotations, to train the unified generation model. 2) We propose a novel hierarchical progressive control strategy that decouples topology clues from visual context by a phased signal injection, mitigating semantic drift while ensuring the topological integrity of sparse structures. 3) We design a sparsity-aware loss re-weighting mechanism to address the extreme sparsity of curvilinear structures, significantly enhancing the attention on thin and fragile structures during optimization. Extensive experiments demonstrate that CSGen generates images with superior structure accuracy and visual realism, significantly improving downstream segmentation performance while maintaining robustness across diverse prompts. Our results confirm CSGen as a scalable, data-centric paradigm for the analysis of complex curvilinear structures in diverse multimedia applications. Code and dataset are available at https://github.com/ShanZard/CSGen.