VEFX-Bench
Benchmarking Generic Video Editing and Visual Effects
📄 Paper • 💻 Code • 🤗 Dataset • 🤗 Model (4B) • 🏆 Leaderboard • 🌐 Project Page
VEFX-Bench is a comprehensive benchmark for evaluating text-driven video editing and visual effects. It includes 5,049 annotated examples spanning 9 categories and 32 subcategories, evaluated by VEFX-Reward — a VLM-based reward model that scores edits across three dimensions on a 1–4 scale:
| Dimension | What it measures |
|---|---|
| Instructional Following (IF) | Does the edit accurately reflect the editing instruction? |
| Render Quality (RQ) | Visual clarity, temporal consistency, and physical plausibility |
| Edit Exclusivity (EE) | Were only the intended regions modified, without side-effects? |
🏆 Model Leaderboard
VEFX-Reward scores on 1–4 scale. Ranked by GeoAgg (α=2 for IF, β=1 for RQ, γ=1 for EE). Higher is better.
📅 Updated: May 2, 2026 — For the latest results & submissions, visit the live leaderboard →
| Rank | Model | Type | IF ↑ | RQ ↑ | EE ↑ | GeoAgg ↑ |
|---|---|---|---|---|---|---|
| 🥇 | Kling o3 Omni | Commercial | 3.033 | 3.588 | 3.043 | 3.057 |
| 🥈 | Kling o1 | Commercial | 3.040 | 3.534 | 2.976 | 2.985 |
| 🥉 | Runway Gen-4.5 | Commercial | 2.817 | 3.319 | 2.923 | 2.912 |
| 4 | Seedance 2.0 | Commercial | 2.811 | 3.421 | 3.088 | 2.766 |
| 5 | Grok Imagine | Commercial | 2.606 | 3.346 | 3.376 | 2.723 |
| 6 | Luma Ray 3 | Commercial | 2.702 | 3.403 | 2.705 | 2.717 |
| 7 | UniVideo | Open-source | 2.294 | 3.266 | 3.091 | 2.516 |
| 8 | Wan 2.6 | Commercial | 2.012 | 3.317 | 2.446 | 2.146 |
| 9 | Luma Ray 2 | Commercial | 2.038 | 2.532 | 1.363 | 1.804 |
| 10 | VACE | Open-source | 2.027 | 3.172 | 1.180 | 1.775 |
🎬 Demo Videos
Each demo shows the original video (left) alongside the edited video (right).
| Attribute Change "Change the color of the red industrial trailer to a bright yellow while maintaining the texture and appearance of the metal surface." |
Object Removal "Remove the woman with the grey backpack walking on the right side of the frame." |
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| Style Transfer "Restore the natural, realistic colors to the entire scene, replacing the current black and white style with a full-color rendition." |
Camera Motion "Perform a smooth zoom in on the distant snowy mountain peaks to create a more immersive view." |
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📊 Benchmark at a Glance
| 📝 5,049 Annotated Examples | 🎬 1,419 Source Videos |
| 📂 9 / 32 Categories / Subcategories | 🤖 10 Editing Systems |
| 📐 3 Quality Dimensions (IF, RQ, EE) | 🧪 300 Benchmark Test Pairs |
🤗 VEFX-Reward Models
| Model | Backbone | Params | HuggingFace | Status |
|---|---|---|---|---|
| VEFX-Reward-4B | Qwen3-VL-4B-Instruct | 4B | xiangbog/VEFX-Reward-4B | ✅ Available |
| VEFX-Reward-32B | Qwen3-VL-32B-Instruct | 32B | TBD | 🔜 Coming soon |
🚀 Quick Start
Installation
conda create -n vefx-bench python=3.10 -y
conda activate vefx-bench
# Install PyTorch first (match your CUDA version)
# See https://pytorch.org/get-started/locally/ for the right command
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu124
# Install remaining dependencies
pip install -r requirements.txt
# Install the package
pip install -e .
Requirements: Python ≥ 3.10, CUDA GPU, ~10 GB VRAM (bfloat16). Make sure your PyTorch CUDA version matches your driver.
Score a Video Edit (Python API)
from vefx_reward import VEFXReward
model = VEFXReward("xiangbog/VEFX-Reward-4B", device="cuda")
scores = model.score(
original_video="examples/sample_videos/object_removal_original.mp4",
edited_video="examples/sample_videos/object_removal_edited.mp4",
instruction="Remove the woman with the grey backpack walking on the right side of the frame.",
)
print(scores)
# {'IF': 2.34, 'RQ': 1.93, 'EE': 1.82, 'Overall': 6.09}
CLI Usage
python examples/quick_start.py \
--original examples/sample_videos/object_removal_original.mp4 \
--edited examples/sample_videos/object_removal_edited.mp4 \
--instruction "Remove the woman with the grey backpack walking on the right side of the frame."
Score All Included Samples
The repo includes 4 sample video pairs with prompts. Score them all:
import json
from vefx_reward import VEFXReward
model = VEFXReward("xiangbog/VEFX-Reward-4B", device="cuda")
with open("examples/sample_videos/prompts.json") as f:
samples = json.load(f)
for sample in samples:
scores = model.score(
original_video=f"examples/sample_videos/{sample['original']}",
edited_video=f"examples/sample_videos/{sample['edited']}",
instruction=sample["instruction"],
)
print(f"[{sample['category']}] IF={scores['IF']:.2f} RQ={scores['RQ']:.2f} EE={scores['EE']:.2f}")
Batch Scoring
Prepare a CSV with columns original_video, edited_video, instruction:
python examples/batch_scoring.py --csv edits.csv --output results.csv
Multi-GPU Scoring
For large-scale evaluation across multiple GPUs:
python examples/multi_gpu_scoring.py --csv edits.csv --num_gpus 4 --output results.csv
📖 API Reference
VEFXReward
VEFXReward(
model_path="xiangbog/VEFX-Reward-4B", # HuggingFace ID or local path
device="cuda", # "cuda", "cuda:0", "cpu"
dtype=torch.bfloat16, # torch.bfloat16 or torch.float16
fps=4.0, # Video sampling rate
max_frame_pixels=399360, # Max pixels per frame
)
model.score(original_video, edited_video, instruction) → dict
Score a single video edit. Returns {'IF': float, 'RQ': float, 'EE': float, 'Overall': float}.
model.score_batch(original_videos, edited_videos, instructions) → list[dict]
Score multiple edits sequentially. Each sample is processed independently to avoid OOM.
📝 Citation
@article{gao2025vefxbench,
title={VEFX-Bench: Benchmarking Generic Video Editing and Visual Effects},
author={Xiangbo Gao and Sicong Jiang and Bangya Liu and Xinghao Chen and Minglai Yang and Siyuan Yang and Mingyang Wu and Jiongze Yu and Qi Zheng and Haozhi Wang and Jiayi Zhang and Jared Yang and Jie Yang and Zihan Wang and Qing Yin and Zhengzhong Tu},
journal={arXiv preprint arXiv:2604.16272},
year={2026}
}
License
This project is licensed under the Apache License 2.0. See LICENSE for details.