Work

Vector Calligrapher: Generating Scalable Vector Graphics via Structured Linguistic Supervision

Year: 2026

Type: conference-paper

Abstract: Generating SVG-based fonts requires Multimodal Large Language Models (MLLMs) to translate high-level linguistic intent into lowlevel, topologically constrained symbolic sequences.However, current approaches struggle with two fundamental misalignments: the semantic ambiguity of unstructured natural language for precise geometric control, and the inefficiency of generic text tokenizers, which fragment coordinate-dense SVG XML into excessively long sequences with low information density.In this work, we propose Vector Calligrapher, a system that treats SVG generation as a conditional language modeling task optimized for both semantic grounding and representational efficiency.

Source: Proceedings of the conference - Association for Computational Linguistics. Meeting

DOI: 10.18653/v1/2026.acl-long.511

Authors: Bo Zhou, Xikang Chen, Yan Gong, Yin Zhang

Institution: Zhejiang University

Language: English

Topic: Generative Adversarial Networks and Image Synthesis

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Natural Science Foundation of China, Ministry of Education, India, Fundamental Research Funds for the Central Universities, Natural Science Foundation of Zhejiang Province

Awards: G2064401243, G2120003303