AGE: Agentic Gaussian Editing in 3D Scenarios

Segmenting thin structures such as infrastructure cracks andanatomical vessels is hampered by topology-sensitive geometry, highannotation cost, and poor cross-domain generalization. We propose FMS2 ,a unified flow-matching framework with two complementary modules.SegFlow is a 2.96M-parameter encoder–decoder segmentation modelthat recasts prediction as continuous image→mask transport: it learnsa time-indexed velocity field with a single flow-matching regression lossand obtains the mask through deterministic ODE integration, ratherthan supervising only end-state logits. This trajectory-level supervisionimproves thin-structure continuity and sharpness over topology-awareloss baselines, without auxiliary topology heads, post-processing, or multi-term loss engineering. SynFlow is a mask-conditioned mask→imagegenerator that produces pixel-aligned image–mask pairs by injectingmask geometry at multiple scales, emphasizing boundary bands withedge-aware gating, and expanding sparsity, width, and branching througha controllable mask generator. On five crack and vessel benchmarks,SegFlow alone outperforms CNN, Transformer, Mamba, and generativebaselines, improving mean IoU from 0.511 to 0.599 (+17.2%) and reducingBetti matching error from 82.145 to 51.524 (−37.3%). With limitedlabels, SynFlow recovers near-full performance using 25% real annotationsand improves cross-domain IoU by 0.11. Unlike classical augmentation,SynFlow provides pixel-aligned supervision with controllable structuralshifts (e.g., sparsity, width, branching), making it effective under domainshift. We release 10k crack and 1k vessel image–mask pairs. The projectpage is available at FMS2 .