code/postereval/README.md

PosterEval Evaluation Code

This package provides the metric code for evaluating generated academic posters. It is organized as a small evaluation toolkit: prepare optional inputs, generate IR when needed, and compute metrics.

What Is Included

• PPTX structural metrics: Ove, Ali, Ofl
• IR generation from poster images
• IR-based content metrics: Order, Completeness, LTA, Claim F1
• OpenRouter-compatible VLM/LLM interface
• Public prompt files and one semantic-metric example config

Metric Inputs

PPTX
  -> evaluate_structural_pptx.py
       -> Ove / Ali / Ofl

poster image
  -> prepare_ir.py --parser content
       -> Order / Completeness / Claim F1
  -> prepare_ir.py --parser figure
       -> LTA

content and figure are intentionally separate IR parsers. The first focuses on section roles, OCR text, and atomic claims. The second focuses on tighter figure grounding for text-figure alignment. The exact prompts are under prompts/.

Metric Protocol

Structural metrics are computed directly from PPTX geometry:

• Ofl: total out-of-canvas shape area divided by canvas area, over all shapes.
• Ali: six-axis nearest-anchor alignment loss over valid visible shapes.
• Ove: mean IoU over valid visible shape pairs after dropping empty Rectangle / Rounded Rectangle background containers and skipping containment pairs where intersection / min(area_i, area_j) >= 0.9.

The valid-visible threshold is 0.1% of canvas area. Container filtering is applied to Ove only; Ali and Ofl keep the direct PPTX shape set.

Semantic metrics use two IR files per poster:

• content IR: Order, Completeness, and Claim F1
• figure IR plus poster image: LTA

The default Claim F1 policy is strict_v2_t05_subset_numeric_one_side85: LLM pair scores, threshold 0.5, greedy one-to-one matching, 1% subset numeric consistency, and a one-side-only numeric high-score waiver at 0.85. The empty-claim fallback follows the local evaluator used for the reported runs: both empty lists score P=R=1, empty generated claims score P=1,R=0, and empty reference claims score P=R=1. Reported benchmark IR records use non-empty reference claim lists; the fallback is retained for compatibility.

Installation

python3 -m pip install -r requirements.txt

Optional dependencies:

• LibreOffice, only if using prepare_pptx_autofit.py.
• A local Qwen3-VL-Embedding-2B checkpoint for LTA. This package includes the lightweight wrapper qwen3_vl_embedding.py; download the model with:

python3 download_qwen3_vl_embedding.py

OpenRouter

IR parsing and Claim F1 use an OpenAI-compatible OpenRouter endpoint.

export OPENROUTER_API_KEY=<your_key>
export OPENROUTER_BASE_URL=https://openrouter.ai/api/v1
export POSTEREVAL_LLM_CACHE_DIR=.cache/postereval_llm

The default model alias is:

qwen3-vl-235b -> qwen/qwen3-vl-235b-a22b-instruct

Data Layout

For PPTX metrics, each method root should contain one directory per poster:

<METHOD_ROOT>/
  sample_001/
    poster.pptx
  sample_002/
    poster.pptx

For IR generation, each image root should contain one directory per poster:

<IMAGE_ROOT>/
  sample_001/
    poster.png
  sample_002/
    poster.png

Flat image roots are also supported with prepare_ir.py --layout flat.

Commands

Structural metrics:

python3 evaluate_structural_pptx.py \
  --pptx-root /path/to/pptx_root \
  --output-dir outputs/structural \
  --pptx-filename poster.pptx \
  --workers 8

Structural metrics can be computed directly from a PPTX root; no config file is required for the common single-root case.

Optional PPTX autofit materialization:

python3 prepare_pptx_autofit.py \
  --src-root /path/to/input_pptx_root \
  --dst-root /path/to/output_pptx_root \
  --pptx-name poster.pptx

IR generation:

python3 prepare_ir.py \
  --input-root /path/to/poster_image_root \
  --output-root outputs/ir/method_a \
  --parser both \
  --layout directories \
  --temperature 0.02 \
  --workers 8

Content metrics:

python3 evaluate_semantic_ir.py \
  --config configs/semantic_ir.example.json \
  --output-dir outputs/semantic

By default, outputs do not include absolute input paths. Add --include-paths only for local debugging.

Outputs

Metric scripts write:

• summary.md
• summary.json
• per_paper.csv
• per_paper.json

prepare_ir.py writes:

• content_ir/<sample>/poster_ir.json
• figure_ir/<sample>/poster_ir.json
• summary.json

Notes

This package contains code, prompts, and an example semantic config only. It does not include datasets, generated posters, rendered images, paper PDFs, API keys, or local runtime artifacts.