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    <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, visual grounding, and prompt-driven amodal occlusion recovery, ensuring each component is geometrically complete. Furthermore, we introduce the Semantic SVG Benchmark with human-annotated semantic groups and novel sub-component metrics (Semantic Recall/Precision, PERE) to explicitly evaluate structural compositionality and functional editability. Experiments show that our natively predicted structures surpass the upper bounds of existing flat-generation methods in both grouping quality and editability, while maintaining state-of-the-art visual fidelity.</abstract>
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%0 Conference Proceedings
%T Compositional SVG Generation via VLM-Driven Hierarchical Semantic Parsing
%A Park, Sehwan
%A Kim, Taehoon
%A Han, Geonhee
%A Kim, Dohyun
%A Kim, Seung Wook
%A Seo, Paul Hongsuck
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F park-etal-2026-compositional
%X 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, visual grounding, and prompt-driven amodal occlusion recovery, ensuring each component is geometrically complete. Furthermore, we introduce the Semantic SVG Benchmark with human-annotated semantic groups and novel sub-component metrics (Semantic Recall/Precision, PERE) to explicitly evaluate structural compositionality and functional editability. Experiments show that our natively predicted structures surpass the upper bounds of existing flat-generation methods in both grouping quality and editability, while maintaining state-of-the-art visual fidelity.
%U https://aclanthology.org/2026.emnlp-main.2375/
%P 53942-53967

Markdown (Informal)

[Compositional SVG Generation via VLM-Driven Hierarchical Semantic Parsing](https://aclanthology.org/2026.emnlp-main.2375/) (Park et al., EMNLP 2026)

• Compositional SVG Generation via VLM-Driven Hierarchical Semantic Parsing (Park et al., EMNLP 2026)

ACL

• Sehwan Park, Taehoon Kim, Geonhee Han, Dohyun Kim, Seung Wook Kim, and Paul Hongsuck Seo. 2026. Compositional SVG Generation via VLM-Driven Hierarchical Semantic Parsing. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 53942–53967, Budapest, Hungary. Association for Computational Linguistics.