README.md

---
license: apache-2.0
language:
- en
tags:
- reward-model
- poster
- graphic-design
- image-quality-assessment
- preference-learning
- qwen3-vl
pipeline_tag: image-to-text
library_name: transformers
---

<div align="center">

<h1>PosterReward: Unlocking Accurate Evaluation for High-Quality Graphic Design Generation</h1>

<img src="assert/posterreward_logo_v1.png" alt="PosterReward Logo" width="200"/>

[![Paper](https://img.shields.io/badge/Paper-PDF-red)](https://alexlai2860.github.io/mypaper/posterreward/PosterReward_Arxiv_released.pdf)
[![Project](https://img.shields.io/badge/Project-Page-black)](https://alexlai2860.github.io/PosterReward/)
[![Code](https://img.shields.io/badge/Code-Repository-blue)](https://github.com/MeiGen-AI/PosterReward)
[![arXiv](https://img.shields.io/badge/arXiv-2603.29855-red)](https://arxiv.org/abs/2603.29855)


</div>

## Overview

PosterReward is a dedicated reward modeling framework for poster assessment. It builds a **70k poster preference dataset** from multi-MLLM consensus and introduces specialized models for poster quality evaluation across five dimensions:

1. **Foundational Visual Quality**
2. **AI Artifacts**
3. **Textual Accuracy**
4. **Prompt Fidelity**
5. **Aesthetic Value**

## Available Models

This repository contains three model variants, all built on **Qwen3-VL-8B**:

| Model | Path | Type | Description |
|-------|------|------|-------------|
| **PosterReward Analyser** | `PosterReward_analyser/` | Generative VLM | Generates detailed multi-dimensional analysis of poster images |
| **PosterReward Scorer** | `PosterReward_scorer/` | Scalar Reward Model | Takes analysis + image and produces a scalar reward score |
| **PosterReward-Lite** | `PosterReward-Lite/` | Scalar Reward Model | Simplified pointwise scorer that omits the analysis module for faster inference |

### PosterReward (Full Pipeline)

The full PosterReward pipeline is a two-stage `analysis -> scoring` process:
1. **PosterReward Analyser** generates a detailed textual analysis across five quality dimensions.
2. **PosterReward Scorer** takes the analysis together with the image and outputs a scalar reward score.

### PosterReward-Lite

A simplified variant that directly predicts a scalar reward from the image and prompt, without requiring a separate analysis step. Faster inference at the cost of slightly lower accuracy.

## Benchmark Data

This repository also hosts the **PosterRewardBench** benchmark images:

| File | Description | Size |
|------|-------------|------|
| `PRB_basic_images.tar.gz` | PosterRewardBench-Basic images (1,034 images from Flux, Flux-Krea, SD3.5-L) | ~1.1 GB |
| `PRB_advanced_images.tar.gz` | PosterRewardBench-Advanced images (2,446 images from Seedream-3.0, Seedream-4.0, Qwen-Image-Lightning) | ~736 MB |

Download and extract these archives into the `poster_reward_bench/` directory of the [code repository](https://github.com/MeiGen-AI/PosterReward).

## Quick Start

### Environment Setup

```bash
git clone https://github.com/MeiGen-AI/PosterReward.git
cd PosterReward

cd swift && pip install -e . && cd ..
pip install msgspec "qwen_vl_utils>=0.0.14" torchvision diffusers pillow
```

### PosterReward-Lite (Fast Pointwise Scoring)

```python
from swift.llm import PtEngine, InferRequest

model_path = "path/to/PosterReward-Lite"  # or download from this repo
engine = PtEngine(model_path, max_batch_size=64, task_type='seq_cls', num_labels=1)

messages = [
    {"role": "user", "content": "<image>Your poster description prompt here."},
    {"role": "assistant", "content": ""}
]
request = InferRequest(messages=messages, images=["path/to/poster.png"])

resp_list = engine.infer([request])
score = resp_list[0].choices[0].message.content
print(f"Reward Score: {score}")
```

### Full PosterReward (Two-Stage Pipeline)

```bash
# Edit model paths in inference_posterreward.sh, then:
bash inference_posterreward.sh
```

## Results

### Pointwise Reward Models on PosterRewardBench

| Model | MMRB2 ↑ | HPDv3 ↑ | PRB-Basic ↑ | PRB-Ad ↑ |
|-------|---------|---------|-------------|----------|
| ImageReward | 53.0 | 58.6 | 60.7 | 49.3 |
| PickScore | 57.6 | 65.6 | 66.7 | 44.1 |
| HPSv2 | 55.0 | 65.3 | 70.8 | 43.7 |
| HPSv3 | 58.5 | 76.9 | 72.9 | 41.2 |
| **PosterReward-Lite** | **60.5** | **77.1** | **83.9** | **85.0** |
| **PosterReward** | **59.6** | **77.8** | **86.7** | **86.0** |

## Citation

Coming Soon!

## Acknowledgments

- Thanks to our collaborators and affiliated institutions.
- Thanks to the open-source community and prior reward modeling research.