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README.md

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----
-license: apache-2.0
----
+---
+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)
+
+**CVPR 2026**
+
+</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.