🤗 MotionRAG Model Checkpoints

📋 Overview

MotionRAG is a retrieval-augmented framework for image-to-video generation that significantly enhances motion realism by transferring motion priors from relevant reference videos. Our approach addresses the fundamental challenge of generating physically plausible and semantically coherent motion in video generation.

Our model checkpoints are organized into three key components for each base model:

1. Motion Projector (Resampler): Compresses high-dimensional motion features from the video encoder into compact token representations.

2. Motion Context Transformer: Adapts motion patterns through in-context learning using a causal transformer architecture.

3. Motion-Adapter: Injects the adapted motion features into the base image-to-video generation models.

📦 Checkpoint Files

MotionRAG Enhanced Models

Model	Component	File
CogVideoX	CogVideoX-5B 17 frames	checkpoints/CogVideoX/17_frames.ckpt
CogVideoX	Motion Projector	checkpoints/CogVideoX/motion_proj.ckpt
CogVideoX	Motion Context Transformer	checkpoints/CogVideoX/motion_transformer.ckpt
CogVideoX	Motion-Adapter	checkpoints/CogVideoX/Motion-Adapter.ckpt
DynamiCrafter	Motion Projector	checkpoints/DynamiCrafter/motion_proj.ckpt
DynamiCrafter	Motion Context Transformer	checkpoints/DynamiCrafter/motion_transformer.ckpt
DynamiCrafter	Motion-Adapter	checkpoints/DynamiCrafter/Motion-Adapter.ckpt
SVD	Motion Projector	checkpoints/SVD/motion_proj.ckpt
SVD	Motion Context Transformer	checkpoints/SVD/motion_transformer.ckpt
SVD	Motion-Adapter	checkpoints/SVD/Motion-Adapter.ckpt

Datasets

Our dataset differs from OpenVid-1M datasets through curation and preprocessing. We use Llama3.1 to refine captions and extract motion-specific descriptions, which are stored in the motion_caption field. The data is then partitioned into non-overlapping training and test sets.

Dataset	Description	File
OpenVid-1M	Large-scale video dataset for training	datasets/OpenVid-1M/data/openvid-1m.parquet
OpenVid-1K	Test set sampled from OpenVid-1M	datasets/OpenVid-1M/data/openvid-1k.parquet

🚀 Usage

For detailed usage instructions, please refer to the official repository: https://github.com/MCG-NJU/MotionRAG

📝 Citation

If you use these models in your research, please cite our paper:

@inproceedings{zhu2025motionrag,
  title={MotionRAG: Motion Retrieval-Augmented Image-to-Video Generation},
  author={Zhu, Chenhui and Wu, Yilu and Wang, Shuai and Wu, Gangshan and Wang, Limin},
  journal={arXiv preprint arXiv:2509.26391},
  year={2025},
  url={https://arxiv.org/abs/2509.26391}, 
}

📬 Contact

For questions or issues related to the models, please open an issue on the repository.