figmirror

• Jun 1, 2026 — FigMirror release: shipped FigMirror as Claude Code and Codex skills, with a local Web UI for upload, iteration browsing, and refinement.
• Jun 17, 2026 — Algorithm update: refined the Codex path with role-separated Drawer / Reviewer agents, far-near visual review views, reviewer bounding boxes, and annotated feedback passed into the next iteration.
• Jul 1, 2026 — Evaluation update: built an internal hybrid style scorer for repeatable method comparison.
• Future — Paper release: release the scorer protocol, benchmark design, baselines, and analysis.

FigMirror uses a reference figure as the style target, then renders your data through an iterative Drawer / Reviewer loop until the output looks like it belongs in the same paper family.

Already inside Claude Code or Codex? Paste this and let the agent do the setup:

Install FigMirror for me: https://github.com/VILA-Lab/FigMirror

Use this when you want upload, preview, iteration history, and refinement in a browser.

If uv is missing: python3 -m pip install uv.

git clone https://github.com/VILA-Lab/FigMirror.git && cd FigMirror
bash scripts/install.sh
uv run python scripts/figcopy_serve.py --workspace .artifacts/figmirror-workspace --backend codex

Open http://127.0.0.1:8765/.

The installer auto-detects Claude Code and Codex. For Codex, it installs the figmirror skill plus the figmirror-drawer and figmirror-reviewer custom agents used by the current role-separated algorithm.

Use this when you want FigMirror inside your agent, no web UI.

curl -fsSL https://raw.githubusercontent.com/VILA-Lab/FigMirror/main/scripts/install.sh | bash

Target one runtime explicitly:

curl -fsSL https://raw.githubusercontent.com/VILA-Lab/FigMirror/main/scripts/install.sh | bash -s -- --codex
curl -fsSL https://raw.githubusercontent.com/VILA-Lab/FigMirror/main/scripts/install.sh | bash -s -- --claude
curl -fsSL https://raw.githubusercontent.com/VILA-Lab/FigMirror/main/scripts/install.sh | bash -s -- --all

Then attach a paper-figure screenshot, paste your data, and ask:

Use FigMirror to mirror this figure's style with my data.

Need manual target selection, Claude backend, or troubleshooting? See Detailed Install.

Illustration of FigMirror. The left panel shows the core agentic loop; the right panel introduces Grounded Measurement.

FigMirror uses an agentic Drawer-Reviewer loop. In the current Codex path, a top-level Orchestrator delegates drawing to a named Drawer agent and visual audit to a named Reviewer agent. The Reviewer sees a far-view composite plus full-resolution source and candidate views, returns structured feedback with bounding boxes, and the Orchestrator turns that feedback into an annotated image and notes for the next Drawer pass.

The Drawer renders a candidate figure with Grounded Measurement. The Reviewer compares it with the reference image, then returns a visual review, a revision checklist, and a preserve list. The preserve list accumulates across iterations as an anchor against style drift. The Aesthetic Lib provides fallback principles, style rules, and figure properties when the agents disagree or the Drawer has low confidence.

For 3D figures, FigMirror adds geometry-aware prompting for camera, scale, surfaces, lighting, and repair checks, helping the loop preserve the 3D composition of the reference figure while producing editable matplotlib code.

Grounded Measurement builds on two properties of computer-use-trained foundation models: Measurement with Axis, which lets the model return x/y coordinates for visual targets; and Resonate with Code, which turns those coordinates into executable checks, such as cropping a line segment and reading its color from pixels.

For the detailed algorithm, architecture, product envelope, and spec map, read docs/method.md. For the web UI, see scripts/README_figcopy_serve.md.

FigMirror welcomes contributions!

Open an issue for bugs, broken installs, or figure cases FigMirror should learn from; open a PR for showcase examples, prompt improvements, UI polish, or small regression tests.

• Showcase cases: add reference/output pairs that prove the method across chart families.
• UI polish: make the web workflow feel instant, legible, and forgiving.
• Prompt and reviewer quality: improve the Drawer / Reviewer loop without weakening the grounding rules.
• Evaluation: add reproducible cases that catch visual drift, floor violations, and broken exports.

Start with docs/contributing.md. Good first PRs include adding a showcase example, improving a web UI interaction, tightening install docs, or adding a small regression test around runner behavior.

• Ship the reference-to-figure loop as Codex and Claude Code skills.
• Add a local Web UI for upload, iteration browsing, and refinement.
• Publish a 139-figure gallery so users can start without hunting for references.
• Refine the Codex algorithm with role-separated Drawer / Reviewer agents and bbox-annotated visual feedback.
• Build an internal hybrid style scorer for repeatable method comparison.
• Release the scorer protocol, benchmark design, baselines, and analysis in a paper.
• Curate a public benchmark set with reference figures, input data, generated outputs, and human preference labels.
• Harden install and runtime docs for more OpenAI-compatible and local-agent backends.