SOURCE-LINKED INTELLIGENCE

SVG-Score: Human-Aligned Evaluation of Text-to-SVG Generation

arXiv · AI, language, vision and robotics · article · Sep 3, 2026 · UTC

Scalable Vector Graphics (SVG) generation is attracting increasing attention as generative models improve in expressiveness and controllability. Progress, however, is held back by the lack of domain-specific evaluation protocols: current practice relies on metrics designed for natural images, most notably CLIPScore, which was never trained on vector graphics and aligns only partially with human judgment. We introduce \textbf{\ours}, a human-aligned evaluation framework for text-to-SVG generation. Through controlled caption and image perturbations, we first show that CLIP-based scores barely re

Read original source ↗ Open in workspace

recordType

paper

region

Global