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Improving Video Generation with Human Feedback

       
   

📖 Introduction

This repository open-sources the VideoReward component -- our VLM-based reward model introduced in the paper Improving Video Generation with Human Feedback. For Flow-DPO, we provide an implementation for text-to-image tasks here.

VideoReward evaluates generated videos across three critical dimensions:

• Visual Quality (VQ): The clarity, aesthetics, and single-frame reasonableness.
• Motion Quality (MQ): The dynamic stability, dynamic reasonableness, naturalness, and dynamic degress.
• Text Alignment (TA): The relevance between the generated video and the text prompt.

This versatile reward model can be used for data filtering, guidance, reject sampling, DPO, and other RL methods.

📝 Updates

• [2025.08.14]: 🔥 We provide the prompt sets used to evaluate video generation performance in this paper, including VBench, VideoGen-Eval, and TA-Hard. See ./datasets/video_eval_prompts for details.
• [2025.07.17]: 🔥 Release the Flow-DPO.
• [2025.02.08]: 🔥 Release the VideoGen-RewardBench and Leaderboard.
• [2025.02.08]: 🔥 Release the Code and Checkpoints of VideoReward.
• [2025.01.23]: Release the Paper and Project Page.

🚀 Quick Started

1. Environment Set Up

Clone this repository and install packages.

git clone https://github.com/KwaiVGI/VideoAlign
cd VideoAlign
conda env create -f environment.yaml
conda activate VideoReward
pip install flash-attn==2.5.8 --no-build-isolation

2. Download Pretrained Weights

Please download our checkpoints from Huggingface and put it in ./checkpoints/.

cd checkpoints
git lfs install
git clone https://huggingface.co/KwaiVGI/VideoReward
cd ..

3. Scoring for a single prompt-video item.

python inference.py

✨ Eval the Performance on VideoGen-RewardBench

1. Download the VideoGen-RewardBench and put it in ./datasets/.

cd dataset
git lfs install
git clone https://huggingface.co/datasets/KwaiVGI/VideoGen-RewardBench
cd ..

2. Start inference

python eval_videogen_rewardbench.py

🏁 Train RM on Your Own Data

1. Prepare your own data as the instruction stated.

2. Start training!

sh train.sh

🤗 Acknowledgments

Our reward model is based on QWen2-VL-2B-Instruct, and our code is build upon TRL and Qwen2-VL-Finetune, thanks to all the contributors!

⭐ Citation

Please leave us a star ⭐ if you find our work helpful.

@article{liu2025improving,
  title={Improving video generation with human feedback},
  author={Liu, Jie and Liu, Gongye and Liang, Jiajun and Yuan, Ziyang and Liu, Xiaokun and Zheng, Mingwu and Wu, Xiele and Wang, Qiulin and Qin, Wenyu and Xia, Menghan and others},
  journal={arXiv preprint arXiv:2501.13918},
  year={2025}
}

@article{liu2025flow,
  title={Flow-grpo: Training flow matching models via online rl},
  author={Liu, Jie and Liu, Gongye and Liang, Jiajun and Li, Yangguang and Liu, Jiaheng and Wang, Xintao and Wan, Pengfei and Zhang, Di and Ouyang, Wanli},
  journal={arXiv preprint arXiv:2505.05470},
  year={2025}
}