VecGlypher: Unified Vector Glyph Generation with Language Models

Xiaoke Huang; Bhavul Gauri; Kam Woh Ng; Tony Ng; Mengmeng Xu; Zhiheng Liu; Weiming Ren; Zhaochong An

cs.CLFeb 25, 2026

VecGlypher: Unified Vector Glyph Generation with Language Models

Authors: Xiaoke Huang, Bhavul Gauri, Kam Woh Ng, Tony Ng, Mengmeng Xu, Zhiheng Liu, Weiming Ren, Zhaochong An,

Organizations: Meta AI · UC, Santa Cruz

Abstract

Vector glyphs are the atomic units of digital typography, yet most learning-based pipelines still depend on carefully curated exemplar sheets and raster-to-vector postprocessing, which limits accessibility and editability. We introduce VecGlypher, a single multimodal language model that generates high-fidelity vector glyphs directly from text descriptions or image exemplars. Given a style prompt, optional reference glyph images, and a target character, VecGlypher autoregressively emits SVG path tokens, avoiding raster intermediates and producing editable, watertight outlines in one pass. A typography-aware data and training recipe makes this possible: (i) a large-scale continuation stage on 39K noisy Envato fonts to master SVG syntax and long-horizon geometry, followed by (ii) post-training on 2.5K expert-annotated Google Fonts with descriptive tags and exemplars to align language and imagery with geometry; preprocessing normalizes coordinate frames, canonicalizes paths, de-duplicates families, and quantizes coordinates for stable long-sequence decoding. On cross-family OOD evaluation, VecGlypher substantially outperforms both general-purpose LLMs and specialized vector-font baselines for text-only generation, while image-referenced generation reaches a state-of-the-art performance, with marked gains over DeepVecFont-v2 and DualVector. Ablations show that model scale and the two-stage recipe are critical and that absolute-coordinate serialization yields the best geometry. VecGlypher lowers the barrier to font creation by letting users design with words or exemplars, and provides a scalable foundation for future multimodal design tools.

Figures & tables

Command	Arguments	Description
M / m (MoveTo)	$x$ , $y$	Move the cursor to the end-point $(x,y)$ without drawing anything.
L / l (LineTo)	$x$ , $y$	Draw a line to the end-point $(x,y)$ .
Q / q (Quadratic Bézier)	$q_{x}$ , $q_{y}$ $x$ , $y$	Draw a quadratic Bézier curve with the control point $(q_{x},q_{y})$ and the end-point $(x,y)$ .
Z / z (ClosePath)	$\varnothing$	Close the path by moving the cursor back to the starting position (ignorable).

Table 1: SVG draw commands used in this work. We serialize each glyph as a single SVG path using only MoveTo , LineTo , Quadratic Bézier , and ClosePath . Uppercase commands use absolute coordinates; lowercase are relative to the current point. Coordinates are quantized to one decimal; other attributes (e.g., “fill”, “stroke”, “transform”) are unused.

Figure 2: Paradigm comparisons. a) Prior image-referenced pipelines use separate image and vector encoder–decoders and a geometry post-optimizer. b) Diffusion-based approaches cascade image diffusion with a vector decoder. c) VecGlypher unifies both text- and image-referenced conditioning within a single LLM: given a style description or reference glyph images plus a target character, the model autoregressively emits SVG path tokens that detokenize to a valid SVG path. This formulation removes raster intermediates and exemplar-sheet requirements while producing directly editable vectors. A practical workflow is to first generate a few reference glyphs from text descriptions, then bootstrap with those images to synthesize the full font.

Table 5: Dataset statistics. Word clouds (left) visualize tag vocabularies for Google Fonts (expert-curated, appearance-focused) versus Envato (noisier, marketing-oriented). The middle plots show the per-font number of tags (Google: concise; Envato: capped at

<

15). Right: distributions of input token length (tags) and output token length (SVG path tokens), with Envato exhibiting heavier-tailed outputs. These differences motivate our two-stage recipe: large-scale continuation on Envato, then instruction alignment on Google Fonts.

Figure 3: VecGlypher pipeline and training recipe. a) A text tokenizer or image encoder condition the LLM ( $||$ denotes mutually exclusive choice), which predicts the next SVG token until the path is produced; detokenization yields SVG paths that we rasterize for display only. b) Training is two-stage: 1) SFT on Envato (text-referenced only) teaches SVG syntax and long-horizon geometry; 2) SFT on Google Fonts (text- or image-referenced) aligns geometry to appearance instructions. No raster denoisers or post-optimizers are used.

Figure 4: Text-referenced ablations. Representative text-to-glyph generations across model sizes and data regimens: ground truth (GT), Google-only 4B and 27B models, Envato-only 27B, mixed E+G 27B, and two-stage E

\to

G 27B. Using the same style tags across columns, scaling and the two-stage recipe yield cleaner closures, stable counters, and more faithful style. Please refer to the supplementary materials for comprehensive results.

Data	Size	Repr.	R-ACC $\,\uparrow$	CD $\,\downarrow$	CLIP $\,\uparrow$	DINO $\,\uparrow$	FID $\,\downarrow$
Google	4B	R	73.96	4.29	26.02	90.27	15.57
		A	66.66	3.75	26.04	89.92	14.69
	27B	R	92.81	2.31	26.38	93.01	5.81
		A	94.91	1.98	26.43	93.43	3.96
Envato	27B	R	93.99	3.89	26.43	89.79	30.68
		A	93.84	3.63	26.45	90.24	20.43

Table 6: Text-referenced ablations on data and model size. We compare relative (R) vs. absolute (A) coordinate serialization and several data regimens: Google-only (G), Envato-only (E), mixed E+G, and two-stage E

\to

G. Scaling and the two-stage E

\to

G training with absolute coordinates yield the best geometry and fidelity.

Data	Size	Repr.	R-ACC $\,\uparrow$	CD $\,\downarrow$	CLIP $\,\uparrow$	DINO $\,\uparrow$	FID $\,\downarrow$
Google	4B	R	83.48	2.36	25.90	93.26	9.38
		A	81.94	2.11	25.89	93.11	7.94
	27B	R	94.42	1.53	26.03	94.88	4.07
		A	96.46	1.41	26.04	95.15	2.84
E $\to$ G I	27B	R	98.90	1.17	26.06	95.69	2.51
		A	97.88	1.16	26.06	95.84	2.55

Table 7: Image-referenced ablations on data and model size. With 1—8 reference glyphs, we study Google-only 4B/27B and two-stage variants that first SFT on Envato then fine-tune on Google (“E

\to

G I” for image-only training; “E

\to

G T,I” for mixed training). At 27B, absolute coordinates achieve the strongest overall results, outperforming single-stage training.

Model	R-ACC $\,\uparrow$	CD $\,\downarrow$	CLIP $\,\uparrow$	DINO $\,\uparrow$	FID $\,\downarrow$
GPT-5 Mini	4.17	10.70	24.75	79.65	63.86
Gemini-2.5 Flash	4.89	13.78	25.26	78.62	86.03
Claude Haiku 4.5	32.38	7.60	25.61	84.69	35.07
GPT-5	43.98	6.12	25.95	86.92	29.00
Gemini 2.5 Pro	24.04	8.22	25.57	83.77	47.82
Claude Sonnet 4.5	46.65	5.28	25.99	88.31	19.59

Table 8: Text-referenced comparisons with general LLMs. We evaluate several proprietary/budget LLMs against VecGlypher on cross-family OOD fonts. Budget models rarely emit valid SVG; flagship models improve but still trail our approach. VecGlypher-70B (A) attains state-of-the-art performance.

Model	R-ACC $\,\uparrow$	CD $\,\downarrow$	CLIP $\,\uparrow$	DINO $\,\uparrow$	FID $\,\downarrow$
DeepVecFont-v2	37.86	14.58	24.81	79.41	115.5
DualVector	49.20	16.45	25.07	79.57	105.5
VecGlypher 27B T,I,R	99.08	1.21	26.07	95.65	2.48
VecGlypher 27B T,I,A	99.12	1.18	26.07	95.82	2.32

Table 9: Image-referenced comparisons with vector-font baselines. Against DeepVecFont-v2 ( Wang et al., 2023 ) and DualVector ( Liu et al., 2023b ) , VecGlypher-27B (T,I) delivers substantially higher recognizability and drastically lower geometric and distributional errors, demonstrating strong generalization to unseen styles.

Figure 5: Image-referenced ablations. Given 1–8 reference glyphs from a font, we compare Google-only 4B/27B with two-stage variants (E

\to

G I and E

\to

G T,I). The two-stage settings at 27B best transfer style and preserve thin structures and closures; Google-only baselines underperform in geometry. Please refer to the supplementary materials for comprehensive results.

Figure 6: Text-referenced comparisons to general LLMs. Rows show GT, three flagship/budget multimodal LLMs, and our VecGlypher models. General LLMs often fail to output valid, closed paths or the requested character, whereas VecGlypher (27B/70B) consistently produces watertight, stylistically faithful vectors for the same prompts. More results can be found in the supplementary materials.

Figure 7: Image-referenced comparisons to vector-font baselines. On unseen font families, DeepVecFont-v2 ( Wang et al., 2023 ) and DualVector ( Liu et al., 2023b ) struggle with thin strokes and contour closure. VecGlypher-27B (T,I) preserves delicate structures, follows target content, and closes contours. Please refer to the supplementary materials for the comprehensive comparisons.

Appendix figures & tables7 assets

Supplementary material from the paper’s appendix.

Appendix

Data	Size	Repr.	R-ACC	CD	CLIP	DINO	FID	FID (C)	R-ACC (U)	CD (T)	CD (ST)	L2	LPIPS	PSNR	SSIM
Google	4B	Rel.	73.96	4.29	26.02	90.27	15.57	1.84	75.51	3.83	4.40	0.196	0.245	8.30	0.643
	4B	Abs.	66.66	3.75	26.04	89.92	14.69	2.19	68.68	3.36	3.85	0.205	0.251	8.14	0.631
	27B	Rel.	92.81	2.31	26.38	93.01	5.81	0.440	93.40	1.96	2.32	0.158	0.212	9.21	0.679
	27B	Abs.	94.91	1.98	26.43	93.43	3.96	0.250	95.30	1.69	2.00	0.152	0.204	9.50	0.686
Envato	27B	Rel.	93.99	3.89	26.43	89.79	30.68	1.63	94.86	3.35	3.90	0.211	0.265	7.46	0.621
Envato	27B	Abs.	93.84	3.63	26.45	90.24	20.43	1.31	95.18	3.22	3.64	0.197	0.254	7.78	0.636

Appendix

Table 10: Text-referenced ablations on data and model size.

Data	Size	Repr.	R-ACC	CD	CLIP	DINO	FID	FID (C)	R-ACC (U)	CD (T)	CD (ST)	L2	LPIPS	PSNR	SSIM
Google	4B	Rel.	83.48	2.36	25.90	93.26	9.38	1.08	84.66	1.97	2.45	0.155	0.206	9.60	0.684
	4B	Abs.	81.94	2.11	25.89	93.11	7.94	1.07	83.53	1.77	2.19	0.156	0.205	9.58	0.682
	27B	Rel.	94.42	1.53	26.03	94.88	4.07	0.293	95.30	1.24	1.50	0.127	0.179	10.65	0.716
	27B	Abs.	96.46	1.41	26.04	95.15	2.84	0.147	97.03	1.12	1.34	0.121	0.172	10.79	0.722
E-G (I)	27B	Rel.	98.90	1.17	26.06	95.69	2.51	0.116	99.26	0.910	1.09	0.109	0.159	11.22	0.736
E-G (I)	27B	Abs.	97.88	1.16	26.06	95.84	2.55	0.101	98.43	0.910	1.09	0.107	0.156	11.36	0.740

Appendix

Table 11: Image-referenced ablations on data and model size.

Model	R-ACC	CD	CLIP	DINO	FID	FID (C)	R-ACC (U)	CD (T)	CD (ST)	L2	LPIPS	PSNR	SSIM
GPT-5 mini	4.17	10.70	24.75	79.65	63.86	13.78	4.96	10.49	10.74	0.382	0.412	4.45	0.432
Gemini-2.5 Flash	4.89	13.78	25.26	78.62	86.03	12.77	7.15	13.38	13.78	0.364	0.379	4.85	0.480
Claude Haiku 4.5	32.38	7.60	25.61	84.69	35.07	5.85	38.80	7.17	7.60	0.289	0.332	5.96	0.545
GPT-5	43.98	6.12	25.95	86.92	29.00	3.71	48.03	5.26	6.26	0.301	0.346	5.68	0.521
Gemini 2.5 Pro	24.04	8.22	25.57	83.77	47.82	7.39	27.06	7.72	8.22	0.319	0.347	5.48	0.518
Claude Sonnet 4.5	46.65	5.28	25.99	88.31	19.59	2.81	52.99	4.58	5.34	0.253	0.305	6.62	0.580

Appendix

Table 12: Text-referenced comparisons with general LLMs.

Model	R-ACC	CD	CLIP	DINO	FID	FID (C)	R-ACC (U)	CD (T)	CD (ST)	L2	LPIPS	PSNR	SSIM
DeepVecFont-v2	37.86	14.58	24.81	79.41	115.5	16.45	49.29	14.58	14.58	0.243	0.320	6.70	0.599
DualVector	49.20	16.45	25.07	79.57	105.5	14.73	65.59	16.44	16.44	0.190	0.293	7.99	0.653
VecGlypher 27B T,I,R	99.08	1.21	26.07	95.65	2.48	0.120	99.44	0.950	1.13	0.112	0.162	11.38	0.734
VecGlypher 27B T,I,R	99.12	1.18	26.07	95.82	2.32	0.095	99.82	0.930	1.10	0.110	0.158	11.35	0.736

Appendix

Table 13: Image-referenced comparisons with vector-font baselines.

Model	R-ACC	CD	CLIP	DINO	FID	FID (C)	R-ACC (U)	CD (T)	CD (ST)	L2	LPIPS	PSNR	SSIM
Llama3.3 70B Instruct	0.08	38.28	24.84	75.85	143.73	21.49	0.08	38.26	38.30	0.374	0.388	4.64	0.480
Gemma3 27B IT	0.12	17.90	24.78	74.26	162.89	23.24	0.11	17.44	17.90	0.407	0.397	4.28	0.450
Qwen3 30B A3B Instruct 2507	0.39	26.15	24.66	74.03	148.78	22.47	0.55	26.07	26.15	0.418	0.422	4.219	0.427
OmniSVG	0.01	63.70	23.26	69.35	229.38	35.75	0.01	63.69	63.69	0.496	0.452	3.50	0.356

Appendix

Table 14: Performance of the open weight LLMs and the vector graphic LLM.

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VecFontLLM: Anchor-Guided Direct Synthesis of Chinese Vector Fonts

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Fuzhou University, China · Peking University, China

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Open paper AskPDF arXiv

Direct generation of Chinese vector fonts is a challenging and ongoing problem. A Chinese vector glyph contains complex component structure, anchor layout, and Bézier curve details, which work at different scales, but a standard vector sequence writes them together in one long sequence, making the task of vector font synthesis challenging. Existing direct vector generators often fail on complex characters, while raster-domain methods must vectorize the synthesized glyph images afterward. To address the above-mentioned problem, this paper proposes VecFontLLM, an anchor-guided multimodal large language model for direct few-shot synthesis of Chinese vector fonts. Our key idea is to generate vector glyphs through anchors rather than a standard vector sequence. Specifically, the proposed VecFontLLM first predicts and refines an anchor scaffold that fixes the coarse layout of components and contours, and then completes Bézier control points to recover local curvature and style. At test time, a confidence-guided generation chain samples multiple component candidates and continues synthesis from the highest-confidence one, improving stability for complex glyphs. This work demonstrates, for the first time, high-quality few-shot synthesis of complex Chinese vector glyphs directly in the vector domain, without raster generation or vectorization. Experiments on several Chinese font datasets show substantial improvements over existing vector font synthesis methods, competitive glyph rendering quality against raster-domain baselines, and vector command distributions close to real fonts.

Abstract

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