VBVR-Bench

The official evaluation repository for Very Big Video Reasoning (VBVR). A verifiable evaluation framework that moves beyond model-based judging by incorporating rule-based, human-aligned scorers, enabling reproducible and interpretable diagnosis of video reasoning capabilities. This repository accompanies A Very Big Video Reasoning Suite, accepted to ICML 2026.

Overview

VBVR-Bench evaluates video generation models (especially Image-to-Video models) across 100 tasks spanning 5 cognitive categories:

Category	Definition
Abstraction	To find rules from observations and use rules to deduce results.
Knowledge	Propositional truth statements one could utter, either learned or gifted since birth.
Perception	Immediate access to sense datum, no further justification could be provided, i.e. "Here is one hand".
Spatiality	The intuition of the basic properties of our world, such as three-dimensionality and Euclidean-ness.
Transformation	To simulate spatial-temporal continuities with internal models in one’s mind

Tasks are split into two subsets:

• In-Domain (50 tasks, 250 samples) — tasks seen in VBVR-Dataset
• Out-of-Domain (50 tasks, 250 samples) — held-out tasks for generalization testing

Each task has 5 samples, totaling 500 evaluation videos.

---

Quick Start

1. Install

git clone https://github.com/Video-Reason/VBVR-Bench.git
cd VBVR-Bench
pip install -r requirements.txt
# Or install as a package
pip install -e .

2. Download Ground Truth Data

Download the VBVR-Bench ground truth data from Hugging Face:

Dataset:https://huggingface.co/datasets/Video-Reason/VBVR-Bench-Data

You can download it using the Hugging Face CLI:

# Install huggingface_hub if needed
pip install huggingface_hub
# Download the dataset
huggingface-cli download Video-Reason/VBVR-Bench-Data --repo-type dataset --local-dir /path/to/VBVR-Bench

After downloading, you should have the following structure:

/path/to/VBVR-Bench/
├── VBVR-Bench.json # Metadata: prompts, first frames (base64), GT paths
├── In-Domain_50/
│ ├── G-131_select_next_figure_.../
│ │ ├── 00000/
│ │ │ ├── first_frame.png # Input image (first frame)
│ │ │ ├── final_frame.png # Expected final frame
│ │ │ ├── ground_truth.mp4 # Reference video
│ │ │ └── prompt.txt # Text prompt
│ │ ├── 00001/
│ │ └── ... # 5 samples per task
│ └── ... # 50 tasks
└── Out-of-Domain_50/
└── ... # 50 tasks

3. Generate Model Videos (Inference)

Use your Image-to-Video model to generate videos for each sample. Each sample provides:

• first_frame.png — the input image (condition frame)
• prompt.txt — the text prompt

Organize your model outputs in the following structure:

/path/to/model_outputs/
├── In-Domain_50/
│ ├── G-131_select_next_figure_.../
│ │ ├── 00000.mp4 # Generated video for sample 0
│ │ ├── 00001.mp4 # Generated video for sample 1
│ │ ├── 00002.mp4
│ │ ├── 00003.mp4
│ │ └── 00004.mp4 # 5 videos per task
│ └── ... # Same task folders as GT
└── Out-of-Domain_50/
└── ... # Same task folders as GT

Note: The folder names (In-Domain_50/, Out-of-Domain_50/, task names) and video filenames (00000.mp4, etc.) must match the ground truth structure exactly.

You can use VBVR-Bench.json to iterate over all 500 samples for inference. Each entry contains:

• first_image: Base64-encoded first frame (can also load from first_frame_path)
• prompt: Text prompt for generation
• ground_truth_video_path: Relative path to the GT video for reference

4. Run Evaluation

Option A: Single model evaluation (recommended)

python run_evaluation.py \
--model_path /path/to/model_outputs \
--gt_base /path/to/VBVR-Bench

Option B: Batch evaluation (multiple models)

python run_evaluation.py \
--models_base /path/to/all_model_outputs \
--gt_base /path/to/VBVR-Bench
# Or evaluate specific models
python run_evaluation.py \
--models_base /path/to/all_model_outputs \
--gt_base /path/to/VBVR-Bench \
--models model_A model_B

Option C: Install as a pip package

pip install -e .

After installation, two CLI commands are available:

# Single model evaluation
vbvr-evaluate \
--videos_path /path/to/model_outputs \
--gt_path /path/to/VBVR-Bench
# Batch evaluation (multiple models / checkpoints)
vbvr-run-evaluation \
--models_base /path/to/all_model_outputs \
--gt_base /path/to/VBVR-Bench

You can also use it as a Python library:

fromvbvr_benchimportVBVRBenchbench=VBVRBench(
gt_base_path='/path/to/VBVR-Bench',
output_path='./results'
)
results=bench.evaluate(
videos_path='/path/to/model_outputs',
name='my_model'
)
print(f"In-Domain: {results['In_Domain']['mean_score']:.4f}")
print(f"Out-of-Domain: {results['Out_of_Domain']['mean_score']:.4f}")
print(f"Overall: {results['overall']['mean_score']:.4f}")

---

Detailed Usage

run_evaluation.py Arguments

Argument	Description
--model_path	Path to a single model's video directory
--models_base	Base directory containing multiple model folders
--models	Specific model names to evaluate (with --models_base)
--gt_base	(Required) Path to the downloaded VBVR-Bench ground truth data
--output_dir	Output directory for results (default: auto-generated)
--device	cuda or cpu (default: cuda)

evaluate.py Arguments

Argument	Description
--videos_path	(Required) Path to model output videos
--gt_path	(Required) Path to ground truth data
--output_path	Output directory (default: ./evaluation_results/)
--name	Evaluation run name (default: auto-generated)
--split	In-Domain_50, Out-of-Domain_50, or all (default: all)
--tasks	Specific task names to evaluate
--device	cuda or cpu (default: cuda)

Supported Directory Structures

The evaluation scripts auto-detect the following model output structures:

# Standard structure (matches VBVR-Bench data)
model_outputs/
├── In-Domain_50/
│ └── {task_name}/
│ └── {idx}.mp4
└── Out-of-Domain_50/
└── ...
# Checkpoints (auto-detected)
model_outputs/
├── checkpoint-100/
│ ├── In-Domain_50/
│ └── Out-of-Domain_50/
└── checkpoint-200/
└── ...

---

Output Format

Results are saved as JSON with the following structure:

{
"model_name": "my_model",
"summary": {
"In_Domain": { "mean_score": 0.72, "num_samples": 250, "by_task": {...}, "by_category": {...} },
"Out_of_Domain": { "mean_score": 0.65, "num_samples": 250, "by_task": {...}, "by_category": {...} },
"overall": { "mean_score": 0.68, "num_samples": 500, "by_task": {...}, "by_category": {...} }
},
"samples": [
{
"video_path": "...",
"task_name": "G-131_select_next_...",
"split": "In_Domain",
"category": "Abstraction",
"score": 0.85,
"dimensions": { ... }
},
...
]
}

---

Repository Structure

VBVR-Bench/
├── evaluate.py # Main evaluation script (VBVRBench API)
├── run_evaluation.py # Flexible evaluation with auto-detection
├── requirements.txt # Python dependencies
├── setup.py # Package installation
├── task_rules.json # Detailed evaluation rules per task
└── vbvr_bench/
├── __init__.py # VBVRBench class
├── utils.py # Utility functions
└── evaluators/
├── __init__.py # Evaluator registry, task metadata, and split definitions
├── base_evaluator.py
└── ... # Task-specific evaluator modules

---

Citation

@article{vbvr2026,
title = {A Very Big Video Reasoning Suite},
author = {Wang, Maijunxian and Wang, Ruisi and Lin, Juyi and Ji, Ran and Wiedemer, Thadd{\"a}us and Gao, Qingying and Luo, Dezhi and Qian, Yaoyao and Huang, Lianyu and Hong, Zelong and Ge, Jiahui and Ma, Qianli and He, Hang and Zhou, Yifan and Guo, Lingzi and Mei, Lantao and Li, Jiachen and Xing, Hanwen and Zhao, Tianqi and Yu, Fengyuan and Xiao, Weihang and Jiao, Yizheng and Hou, Jianheng and Zhang, Danyang and Xu, Pengcheng and Zhong, Boyang and Zhao, Zehong and Fang, Gaoyun and Kitaoka, John and Xu, Yile and Xu, Hua bureau and Blacutt, Kenton and Nguyen, Tin and Song, Siyuan and Sun, Haoran and Wen, Shaoyue and He, Linyang and Wang, Runming and Wang, Yanzhi and Yang, Mengyue and Ma, Ziqiao and Milli{\`e}re, Rapha{\"e}l and Shi, Freda and Vasconcelos, Nuno and Khashabi, Daniel and Yuille, Alan and Du, Yilun and Liu, Ziming and Lin, Dahua and Liu, Ziwei and Kumar, Vikash and Li, Yijiang and Yang, Lei and Cai, Zhongang and Deng, Hokin},
journal = {arXiv preprint arXiv:2602.20159},
year = {2026},
url = {https://arxiv.org/abs/2602.20159}
}