README.md

metadata

license: cc-by-nc-4.0
pretty_name: PlotTwin-Bench
language:
  - en
task_categories:
  - image-to-text
  - text-to-image
tags:
  - figure-style-transfer
  - chart-to-code
  - scientific-figures
  - matplotlib
size_categories:
  - n<1K
configs:
  - config_name: bench
    default: true
    data_files:
      - split: test
        path: data/bench/*.parquet
  - config_name: full
    data_files:
      - split: test
        path: data/full/*.parquet

PlotTwin-Bench

PlotTwin-Bench is a benchmark for figure style transfer: given a reference figure and a new dataset, produce a plotting script that draws the new data in the reference's visual style. Every reference is a publication-style matplotlib figure shipped with the script that renders it.

• Paper: FigMirror: Ground It, Code It, Plot It
• Code: github.com/VILA-Lab/FigMirror

Configs

config	rows	style-transfer tasks	contents
bench (default)	150	150	the evaluation set: all 50 hand-curated references and 100 augmented references sampled at random
full	399	150	every reference: 50 hand-curated and 349 augmented; the 150 bench rows carry target data

bench is a subset of full, and each bench row appears in full unchanged.

from datasets import load_dataset

bench = load_dataset("figmirror/PlotTwin-Bench", split="test")          # 150 tasks
full = load_dataset("figmirror/PlotTwin-Bench", "full", split="test")   # 399 references
tasks = full.filter(lambda x: x, input_columns="in_bench")              # same 150 tasks

Two sources

Hand-curated (50). Complex figures selected from papers at top venues and journals, each replotted by hand in matplotlib to form an aligned figure–code pair. Every hand-curated row cites the figure it was replotted from (source_url, source_title, source_bibkey; BibTeX in sources.bib). The reference image is our own rendering, not the published figure.

Augmented (349). Plotting code from existing chart-to-code datasets, rewritten by an LLM pipeline into more complex and visually polished figures, then filtered for both properties. Each row records its seed (seed_dataset, seed_url, seed_id).

Task

A task pairs reference_image with target_data, a CSV describing a new study in a different scientific domain. The target data keeps the reference's chart families and panel structure and changes one to three aspects of the data, such as series count, category cardinality, panel allocation or scale type. A method returns a self-contained plotting script that draws target_data in the style of reference_image.

Fields

field	type	description
reference_image	image	the reference figure, rendered by reference_code
id	string	sample id
source	string	hand-curated or augmented
chart_type, chart_type_name	string	one of 12 chart types, T1 Bar … T12 Composite
panel_mode	string	single or multi
in_bench	bool	the row belongs to the bench evaluation set
source_url, source_title, source_bibkey	string	publication a hand-curated reference was replotted from
seed_dataset, seed_url, seed_id	string	upstream seed of an augmented reference
target_data	string	CSV to plot in the reference style; set on the 150 task rows
reference_code	string	matplotlib script that renders reference_image
reference_data	list	data files the script reads (filename, content)

Rendering a reference

reference_code reads its data files from its own directory and saves one PNG.

from pathlib import Path
import subprocess, sys

def materialize(row, workdir):
    workdir = Path(workdir) / row["id"]
    workdir.mkdir(parents=True, exist_ok=True)
    (workdir / "code.py").write_text(row["reference_code"])
    for f in row["reference_data"]:
        (workdir / f["filename"]).write_bytes(f["content"])
    if row["target_data"]:
        (workdir / "target_data.csv").write_text(row["target_data"])
    return workdir

sample = materialize(bench[0], "plottwin")
subprocess.run([sys.executable, "code.py"], cwd=sample, check=True)

The released images were rendered with Python 3.13, matplotlib 3.10.9 and numpy 2.4.4 (Agg backend). Other font sets or library versions can change text rasterisation slightly.

Scoring

The scorer/ folder holds the PlotTwin-Bench scorer (deviation-defect-v1).

• S_code parses the reference and candidate scripts and compares nine style attributes: palette, background, figure aspect, line width, grid, hidden spines, tick direction, serif font and legend frame. An attribute counts only when the reference departs from the matplotlib default.
• S_vision shows a vision model the reference and the candidate image and asks for every reference style choice the candidate misses. Each miss deducts 5, 10 or 25 points (minor, major, critical) from 100.
• S averages each channel over a split and blends them: S = 0.65 · S_vision + 0.35 · S_code.

A prediction is one <id>.py per task. The script reads the target data from data.csv in its working directory and saves one PNG; the scorer renders it. A <id>.png next to the script is used as the candidate image instead.

hf download figmirror/PlotTwin-Bench --repo-type dataset --include "scorer/*" --local-dir plottwin
cd plottwin/scorer
export OPENAI_API_KEY=...        # set OPENAI_BASE_URL for any OpenAI-compatible endpoint
uv run plottwin-score --pred-dir /path/to/predictions --out-dir scores

The vision model defaults to gpt-5.5; set --model and --reasoning-effort to change it. Per-sample results go to scores/per_sample/<id>.json and are reused when a run restarts; scores/summary.json reports S, S_code and S_vision for the hand-curated and augmented splits. examples/quickstart.py scores a default-style baseline on three tasks end to end:

uv run python examples/quickstart.py

Chart types

type	hand-curated	augmented (full)	bench total
T1 Bar	0	140	18
T2 Line	5	70	20
T3 Scatter	5	25	16
T4 Distribution	9	10	17
T5 Box/Violin	5	27	16
T6 Heatmap	4	19	14
T7 Field 2D	5	19	15
T8 3D	4	5	7
T9 Network/Tree	2	3	2
T10 Polar/Radial	1	29	13
T11 Std-band	7	2	9
T12 Composite	3	0	3

License and attribution

PlotTwin-Bench is released under CC BY-NC 4.0. Augmented references derive from the plotting code of these datasets; follow their terms as well:

seed dataset	rows in full	upstream terms
ChartNet	151	non-commercial notice
Chart2Code	151	level 1 follows ChartMimic (Apache-2.0); levels 2–3 state no licence
Chart2NCode	31	CC BY-NC 4.0
ChartMimic	15	Apache-2.0
ChartGen-200K	1	CC BY 4.0

Hand-curated references are our own matplotlib replots; please also cite the original publications listed in sources.bib when you discuss individual figures.

Citation

@misc{zhao2026figmirrorgrounditcode,
  title         = {FigMirror: Ground It, Code It, Plot It},
  author        = {Xiaohan Zhao and Jiacheng Liu and Yaxin Luo and Zhiqiang Shen},
  year          = {2026},
  eprint        = {2608.28814},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  url           = {https://arxiv.org/abs/2608.28814}
}