Jintae-Park/ReDesign-Figma909

ReDesign Figma-909 Benchmark

         

ReDesign turns a single flat raster image back into an editable design: text with real typography, vector shapes (fill/stroke), images, groups, and z-order, exported as an editable JSON hierarchy. When the original file is lost, a flat export no longer says which pixels form which object or how layers stack. ReDesign recovers that structure.

How it works. ReDesign treats a design as a tree of layers and rebuilds it piece by piece, starting from the whole image as the root:

1. Look & decide: a VLM controller examines a region and picks one tool-backed action to break it down (extract text, fork into layers, split, detect & segment, or vectorize).
2. Split coarse → fine: it expands the tree breadth-first, big regions first, then their finer details.
3. Check every step: a modular verifier accepts, prunes, or retries each split, driving the tree toward clean, atomic, editable leaves.

Figma-909 is the evaluation benchmark for ReDesign: 909 real-world Figma Community designs, each a self-contained episode with ground-truth layer decomposition metadata and per-element images, supporting both reconstruction-accuracy and editability evaluation.

📁 The dataset files (metadata, images, attribution) live in the Files and versions tab above. See the ReDesign GitHub repository for the download script and the full pipeline.

License & Attribution

All 909 episodes are licensed under Creative Commons Attribution 4.0 International (CC BY 4.0). Each design was published under CC BY 4.0 by its original author on the Figma Community. We redistribute the derived decomposition data under the same license, with full attribution preserved.

• License coverage: 909 / 909 (100%) CC BY 4.0
• Unique original authors: 288
• Unique Figma Community files: 389

Per-episode attribution (author name, author URL, source URL, license type, license URL) is preserved in every valid_frames/*.json and aggregated in ATTRIBUTIONS.csv. When you use this dataset, please credit the original authors and retain the CC BY 4.0 license and source links.

If you are an author and would like a frame removed, please open an issue on the GitHub repository.

Dataset structure

Each design is one self-contained episode, identified by an episode_id (the valid_frames JSON filename stem, e.g. 1002728450918630649_2_1898). Every episode ships its ground-truth layer decomposition plus all per-element images.

Path	What it holds
valid_frames/<episode_id>.json	Ground-truth metadata: layer tree, geometry, z-order, license, attribution
unit_images/<figma_dir>/	Per-element layer PNGs, the original render, and the GT reconstruction (_reconstructed_*.png, the agent's input)
reconstructed_images/<episode_id>.png	GT reconstruction keyed by episode id (the _bbox variant overlays element boxes)
ATTRIBUTIONS.csv	Per-episode author, source URL, and license

Inside each JSON, unit_images_dir and the per-element image_path fields are relative to the dataset root, so the reconstruction resolves to <root>/<unit_images_dir>/<reconstructed_image_path>.

Usage with ReDesign

# Download
python scripts/download_figma_dataset.py        # -> ./figma_data

# Run the agent on all 909 episodes
python -m ReDesign.run_agent_figma \
    --data_dir figma_data --output_dir outputs/figma_agent

# Reconstruction accuracy
python evaluation/eval_accuracy_baselines_figma.py \
    --figma-data figma_data --models agent \
    --agent-dir outputs/figma_agent \
    --output outputs/eval_accuracy_figma

# Atomic-edit editability (uses the same figma_data; matches auto-precomputed)
REDESIGN_FIGMA_DATA=figma_data REDESIGN_AGENT_DIR=outputs/figma_agent \
    python evaluation/eval_editability_figma.py --models agent

See the ReDesign GitHub repository for the full pipeline (environment, checkpoints, inference, evaluation).

Complete per-episode attribution for all 288 original authors is provided in ATTRIBUTIONS.csv.

Discussion

Questions, feedback, or requests? Open a thread in the Community tab of this dataset. If you are the author of a frame and would like it removed, please start a discussion here or open an issue on the GitHub repository.