CSGen: A Hierarchical Multimodal Diffusion Model for Curvilinear Structure Generation
A new research paper introduces CSGen, a hierarchical multimodal diffusion model designed to generate high-fidelity images with precise curvilinear structures. The model addresses the challenge of controllable generation of images containing curvilinear objects, which are fundamental in multimedia analysis. CSGen is built on three key innovations: a multi-domain and multimodal dataset comprising over 24,000 samples from five domains and seven annotation types; a hierarchical progressive control strategy that decouples topology from visual context through phased signal injection, mitigating semantic drift; and a sparsity-aware loss re-weighting mechanism to handle the extreme sparsity of curvilinear structures. The paper, available on arXiv (2608.04655), was announced as a cross-type submission. The model aims to synthesize images that align with multiple control conditions, potentially benefiting fields such as medical imaging, remote sensing, and computer vision. The research was conducted by an unnamed team and is presented as a preprint, indicating ongoing development in the field of generative models for structured image synthesis.
Key facts
- CSGen is a hierarchical multimodal diffusion model for curvilinear structure generation.
- The model synthesizes high-fidelity images aligned with multiple control conditions.
- A dataset of over 24K samples from 5 domains and 7 annotation types was constructed.
- A hierarchical progressive control strategy decouples topology from visual context.
- A sparsity-aware loss re-weighting mechanism addresses extreme sparsity.
- The paper is available on arXiv with ID 2608.04655.
- The announcement type is cross.
- The research addresses the open challenge of controllable generation of curvilinear structures.
Entities
Institutions
- arXiv