---
title: 'VFig-Data: Diagram-to-SVG Corpus'
url: https://www.emergentmind.com/topics/vfig-data
type: topic
---

# VFig-Data: Diagram-to-SVG Corpus

VFIG-DATA, more precisely **VFig-Data**, is a large-scale corpus of **66 500 high-quality image/SVG pairs** introduced with “VFIG: Vectorizing Complex Figures in SVG with Vision-Language Models” for figure-to-SVG conversion of complex, high-fidelity diagrams [2603.24575]. It was constructed to address the mismatch between the data demands of figure vectorization and the limitations of earlier datasets, which are described as typically small-scale and lacking the complexity of professional diagrams. The corpus combines **approximately 6 500 real-world paper figures** with **approximately 60 000 programmatically generated diagrams**, and it is situated within a broader framework that also includes a coarse-to-fine training curriculum and the **VFIG-BENCH** evaluation suite [2603.24575].

## 1. Corpus definition and source composition

VFig-Data is defined as a paired raster–vector dataset in which each example associates a rasterized figure image with its corresponding clean SVG representation. The paper positions it as the data foundation for recovering editable vector structure from “flat” raster figures such as PNG or JPEG files [2603.24575].

Its core composition is bimodal. The **real-world subset** contains **approximately 6 500 examples** harvested from **arXiv (post-2025)** and the **Paper2Fig** corpus. The **programmatically generated subset** contains **approximately 60 000 examples** produced by rendering synthetic SVGs to PNG. The real-world component targets **flowcharts, AI-architecture diagrams, multi-panel scientific illustrations, process schematics, and block-diagram pipelines**. The synthetic component covers **block graphs, pipelines, network motifs, 3D primitives (cylinders, prisms), “blob” and “cloud” shapes, and abstract data-flow layouts** [2603.24575].

| Source | Approximate count | Content |
|---|---:|---|
| Real-world paper figures | 6 500 | arXiv and Paper2Fig diagrams |
| Programmatically generated diagrams | 60 000 | Synthetic SVGs rendered to PNG |
| Separate training-only SVG mix | 78 K | StarVector and Molmo2-Diagram |

The **≈ 78 K academic SVGs from StarVector and Molmo2-Diagram** are explicitly described as mixed into model training but **separate from the 66 K VFig-Data core**. This distinction is important because it separates the dataset proper from auxiliary training resources.

A central design feature is the emphasis on diagram classes with explicit geometric and topological structure. This is reflected in the real-world filtering policy, the synthetic generation process, and the later use of structure-oriented evaluation metrics. A plausible implication is that VFig-Data is intended less as a generic vectorization corpus than as a specialized resource for **diagrammatic figures with recoverable compositional structure**.

## 2. Curation methodology and synthetic generation

The real-world subset is curated through a two-stage procedure. First, input figures are classified by the VLM **Gemini-3-Flash** into four categories: **KEEP**, **IMAGE**, **MATH**, and **PLOT**. Only **KEEP** images are retained. Second, SVG outputs are code-filtered by grouping geometric elements into **basic primitives** $B=\{\text{rect},\text{circle},\text{ellipse}\}$, **connectors** $K=\{\text{line},\text{polyline}\}$, and **complex shapes** $C=\{\text{path},\text{polygon}\}$. The enforced constraints are:

- $(B+K)/N \ge 0.40$
- $C \le 50$
- $N = B+K+C$

Light cleaning then **normalizes coordinate precision**, **removes redundant metadata**, and **standardizes viewBox/canvas settings** [2603.24575].

The synthetic subset is generated through a more elaborate procedural pipeline. Layouts are selected from **19 human-inspired templates**; with probability **0.3**, two templates are fused into left/right halves with random cross-links. Shape placement uses **18 shape types (12 flat shapes + 6 pseudo-3D)**, with each diagram drawing from a pool of **2–3 types**, and placement is randomized subject to **AABB collision avoidance**. With probability **0.15**, shapes are stacked **2–4 deep** to simulate a 3D effect [2603.24575].

Styling is also randomized. Each shape samples one of **7 fill patterns**—**solid, hatching, dots, crosshatch, horizontal lines, linear gradient, radial gradient**—together with one of **4 border dash styles**—**solid, dashed, dotted, dash–dot**. Stroke widths, corner radii, and color palettes are varied, with **$p=0.6$ for rounding**. Text labels are drawn from a **domain-specific lexicon** and rendered in **1–2 fonts drawn from 8 families** [2603.24575].

Connection routing is parameterized by the number of shapes $n$ and a sampled number of directed edges
$$
c \sim \mathrm{Uniform}(\lfloor n r_l \rfloor,\lfloor n r_h \rfloor), \qquad
r_l \sim \mathrm{Uniform}(0.4,0.6), \qquad
r_h \sim \mathrm{Uniform}(0.6,0.8).
$$
Template hints supply the first links, and the remainder are random unique pairs. Arrows are **straight (60%)** or **quadratic Bézier (40%)**, with sampled stroke width, head size, and dash style, and they attach at shape boundaries via **analytical ray-casting or parametric intersection** [2603.24575].

This generation procedure makes explicit that the dataset is not merely a collection of paired files; it is a controlled distribution over layout, geometry, styling, and connectivity. That, in turn, explains why the dataset supports both supervised fitting of local SVG primitives and later optimization of global structural fidelity in the associated VFIG training pipeline [2603.24575].

## 3. File organization and annotation schema

Each example consists of a raster input and a vector target. The technical summary specifies the following file-level organization:

- **`image.png`**: raster input, **512×512 or similar**
- **`ground-truth.svg`**: cleaned SVG code
- **`metadata.json`**: synthetic subset only

A **suggested** directory layout places real and synthetic subsets in separate subtrees, with images, SVGs, and synthetic metadata stored in distinct folders. Because the summary explicitly labels this layout as “suggested,” it is best understood as a conventional organization rather than a mandatory on-disk specification.

The synthetic subset includes structured metadata per shape and per arrow. The schema includes:

- **shape attributes**: `shape_id`, `type`, `fill_color`, `stroke_color`, `fill_style`, `border_style`, `font_class`, `bbox`, `aspect_ratio`
- **arrow attributes**: `arrow_id`, `src_shape_id`, `dst_shape_id`, `head_present`, `head_size`, `is_curved`, `overlap_penalty`

Allowed values are also partially specified. For example, `fill_style` ranges over **solid, dots, hatching, crosshatch, gradient_…**, `border_style` over **solid, dashed, dotted, dash-dot**, and `font_class` over **serif, sans-serif, monospace** [2603.24575].

The presence of per-shape and per-arrow metadata only for the synthetic subset is methodologically significant. It preserves paired supervision across the full corpus while providing richer structural annotations where exact ground-truth generation histories are available. This suggests a natural division between **direct paired supervision** on all samples and **fine-grained structural supervision or analysis** on the synthetic portion.

## 4. Statistical characterization and quality metrics

VFig-Data is statistically characterized at both the corpus and element levels. Averaged over the **66 K examples**, the mean primitive counts per SVG are:

- **Basic primitives** $B=(\text{rect}+\text{circle}+\text{ellipse})$: **mean ≈ 30**
- **Connectors** $K=(\text{line}+\text{polyline})$: **mean ≈ 10**
- **Complex shapes** $C=(\text{path}+\text{polygon})$: **mean ≈ 5**
- **Text elements** $T$: **mean ≈ 12** [2603.24575]

The all-corpus histogram summary gives **geometric elements** $N=B+K+C$ with **$\mu \approx 45$** and **$\sigma \approx 20$**, and **text elements** with **$\mu \approx 12$** and **$\sigma \approx 6$**. The SVG file-size statistics are **18 KB ($\sigma \approx 7$ KB)** for real-world examples and **9 KB ($\sigma \approx 3$ KB)** for synthetic examples [2603.24575].

For the **real-world subset only (6.5 K examples)**, the figure-type distribution is:

| Figure type | Share |
|---|---:|
| Flowcharts & pipelines | 32% |
| Neural-network architectures | 28% |
| Multi-panel illustrations | 20% |
| Process schematics & block diagrams | 12% |
| Other | 8% |

The corpus also defines explicit quality-oriented metrics. **Element Complexity** is
$$
\mathrm{EC} = \ln\bigl(1 + N + T\bigr),
\qquad N=B+K+C,
$$
with reported **$\mu \approx 4.0$** and **$\sigma \approx 0.3$** on the log scale. **Semantic Cleanliness** and **Path Dominance** are
$$
\mathrm{Clean} = \frac{B+K}{N},
\qquad
\mathrm{PD} = \frac{C}{N} = 1-\mathrm{Clean}.
$$
The **cleanliness distribution** is summarized as **$\mu \approx 0.80$** and **$\sigma \approx 0.10$**, meaning that roughly **80%** of elements are primitives or connectors [2603.24575].

These metrics are directly aligned with the curation policy. High cleanliness and controlled path dominance favor diagrams whose structure remains editable and semantically decomposable in SVG. The summary also lists a **Diversity Score**,
$$
D=-\sum_i p_i \ln p_i,
$$
but labels it **“optional; not in paper but commonly used.”** This suggests that EC, Clean, and PD are the dataset’s canonical metrics, whereas the diversity score should not be treated as a core paper-defined measure.

## 5. Accessibility, licensing, uses, and limitations

The technical summary describes VFig-Data as **licensed under Creative-Commons CC BY 4.0**, with **dataset, code, and evaluation scripts publicly available** at **https://vfig-proj.github.io/** and direct download via **GitHub/GCP bucket links**. It further states that **users must cite He et al. (2025)** when using VFig-Data in publications and that there is **no non-commercial restriction; redistribution permitted under CC BY 4.0 terms**.

The enumerated use cases include:

- **training and evaluation of vision–language models for figure-to-SVG conversion**
- **pretraining vector-graphic synthesis systems**
- **data augmentation for scientific-figure editing tools**
- **benchmarking multi-modal program synthesis models on long-horizon code generation** [2603.24575]

The dataset is introduced together with a model family and evaluation suite. In that broader setting, VFIG is reported to achieve **state-of-the-art performance among open-source models** and to perform **on par with GPT-5.2**, with a **VLM-Judge score of 0.829 on VFIG-BENCH** [2603.24575]. Although that number is a model result rather than a dataset statistic, it clarifies the empirical role of VFig-Data within the full system.

The stated limitations are equally important. The corpus is described as **heavily skewed toward scientific/engineering diagrams**, with **poor coverage of iconography, natural-scene vectorization, or highly textured SVG art**. It also has **limited font variety (8 families)** and **fill styles (7 types)**, and the real-world subset **filtered out mathematical-equation-only figures and plots** [2603.24575]. A plausible implication is that models trained primarily on VFig-Data may generalize best to diagrammatic scientific figures and less well to plot-centric, equation-centric, or stylistically idiosyncratic vector graphics.

## 6. Terminological ambiguity and other uses of “VFIG-DATA”

In the supplied arXiv-derived materials, the label **“VFIG-DATA”** is not unique to the 2026 figure–SVG corpus. It is also attached to three unrelated technical objects.

First, it is used for a technical summary of the **Faint Infrared Grism Survey (FIGS)**, a deep HST **WFC3/IR G102** slitless spectroscopic survey of four deep fields with **continuous $0.80\,\mu\mathrm{m}$–$1.15\,\mu\mathrm{m}$ coverage**, **$\approx 100$ ks** net on-grism integration per field, and a **$3\sigma$ continuum limit of approximately 26 AB mag** [1706.02669]. That usage concerns astronomy data products, extraction pipelines, and FITS delivery conventions rather than SVG vectorization.

Second, the label appears in a summary of **FSVVD**, a **point-cloud-based volumetric-video dataset** with **26 sequences**, **4 categories**, and capture using **six Microsoft Azure Kinect sensors** arranged perimeter-wise [2303.03599]. That usage belongs to immersive multimedia and 3D scene capture.

Third, it is used for a data-valuation framework derived from **“What is my data worth? From data properties to data value”**, where data value is expressed as
$$
\mathit{DataValue}=\sum_{i=1}^{n}\alpha_i V_i,
\qquad
0\le V_i\le 1,
\qquad
\sum_{i=1}^{n}\alpha_i=1,
$$
and computed over **16 high-level facets** [1811.04665]. That usage concerns multi-attribute utility over dataset properties, not figure datasets.

This multiplicity of senses indicates that “VFIG-DATA” is not a semantically stable cross-domain term in secondary summaries. In current research usage tied to the named 2026 VFIG system, however, **VFig-Data** denotes the **66 500-pair figure–SVG corpus** for complex diagram vectorization [2603.24575].

Source: https://www.emergentmind.com/topics/vfig-data