HAbench 0.1.0

Human Preference Aligned Video Generation Benchmark

HABench: Human Preference Aligned Video Generation Benchmark

HABench is a benchmark tool designed to systematically leverage MLLMs across all dimensions relevant to video generation assessment in generative models. By incorporating few-shot scoring and chain-of-query techniques, HA-Video-Bench provides a structured, scalable approach to generated video evaluation.

Evaluation

Multi-Dimensional Evaluation: Supports evaluation across several key dimensions of video generation:

Dimension	Code Path
Image Quality	HAbench/staticquality.py
Aesthetic Quality	HAbench/staticquality.py
Temporal Consistency	HAbench/dynamicquality.py
Motion Effects	HAbench/dynamicquality.py
Object-Class Consistency	HAbench/VideoTextConsistency.py
Video-Text Consistency	HAbench/VideoTextConsistency.py
Color Consistency	HAbench/VideoTextConsistency.py
Action Consistency	HAbench/VideoTextConsistency.py
Scene Consistency	HAbench/VideoTextConsistency.py

Support for Multiple Video Generation Models:

• Lavie
• Pika
• Show-1
• VideocrAfter2
• CogVideoX5B
• Kling
• Gen3

Installation Requirements

• Python >= 3.8
• OpenAI API access Update your OpenAI API keys in config.json:

  {
      "GPT4o_API_KEY": "your-api-key",
      "GPT4o_BASE_URL": "your-base-url",
      "GPT4o_mini_API_KEY": "your-mini-api-key",
      "GPT4o_mini_BASE_URL": "your-mini-base-url"
  }
  

Installation

git clone https://github.com/yourusername/HABench.git
cd HABench
conda env create -f environment.yml
conda activate HABench

Data Preparation

Please organize your data according to the following structure:

/HABench/data/
├── color/                           # 'color' dimension videos
│   ├── cogvideox5b/
│   │   ├── A red bird_0.mp4
│   │   ├── A red bird_1.mp4
│   │   └── ...
│   ├── lavie/
│   │   ├── A red bird_0.mp4
│   │   ├── A red bird_1.mp4
│   │   └── ...
│   ├── pika/
│   │   └── ...
│   └── ...
│
├── object_class/                    # 'object_class' dimension videos
│   ├── cogvideox5b/
│   │   ├── A train_0.mp4
│   │   ├── A train_1.mp4
│   │   └── ...
│   ├── lavie/
│   │   └── ...
│   └── ...
│
├── scene/                           # 'scene' dimension videos
│   ├── cogvideox5b/
│   │   ├── Botanical garden_0.mp4
│   │   ├── Botanical garden_1.mp4
│   │   └── ...
│   └── ...
│
├── action/                          # 'action' 'temporal_consistency' 'motion_effects' dimension videos
│   ├── cogvideox5b/
│   │   ├── A person is marching_0.mp4
│   │   ├── A person is marching_1.mp4
│   │   └── ...
│   └── ...
│
└── overall_consistency/             # 'overall consistency' 'imaging_quality' 'aesthetic_quality' dimension videos
    ├── cogvideox5b/
    │   ├── Close up of grapes on a rotating table._0.mp4
    │   └── ...
    ├── lavie/
    │   └── ...
    ├── pika/
    │   └── ...
    └── ...

Usage

Run the following command to evaluate the dimension you want to evaluate:

python evaluate.py \
 --dimension $DIMENSION \
 --videos_path ./data/{dimension} \
 --config_path ./config.json/