arXiv Artificial Intelligence

Compositional SVG Generation via VLM-Driven Hierarchical Semantic Parsing

2 hours ago 15.09.2026 07:00 👁 2 views
Compositional SVG Generation via VLM-Driven Hierarchical Semantic Parsing

Quick summary

arXiv:2609.14657v1 Announce Type: cross Abstract: While Vision-Language Models (VLMs) excel at visual reasoning, generating structured, editable Scalable Vector Graphics (SVG) remains a fundamental challenge. Existing pipelines predominantly yield flat, semantically agnostic collections of paths, where editing a single object requires manually identifying its constituent paths. To address this, we propose a VLM-driven agentic framework for semantic compositional SVG generation. Our pipeline recursively parses visual scenes into semantic and geometric hierarchies via top-down decomposition, vis

Key takeaways

  • arXiv:2609.14657v1 Announce Type: cross Abstract: While Vision-Language Models (VLMs) excel at visual reasoning, generating structured, editable Scalable Vector Graphics (SVG) remains a fundamental challenge.
  • Existing pipelines predominantly yield flat, semantically agnostic collections of paths, where editing a single object requires manually identifying its constituent paths.
  • To address this, we propose a VLM-driven agentic framework for semantic compositional SVG generation.

Why it matters

“Compositional SVG Generation via VLM-Driven Hierarchical Semantic Parsing” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗