Qnancy/magicmotion

MagicMotion: Controllable Video Generation with Dense-to-Sparse Trajectory Guidance
Quanhao Li*, Zhen Xing*, Rui Wang, Hui Zhang, Qi Dai, and Zuxuan Wu
* equal contribution

💡 Abstract

Recent advances in video generation have led to remarkable improvements in visual quality and temporal coherence. Upon this, trajectory-controllable video generation has emerged to enable precise object motion control through explicitly defined spatial paths. However, existing methods struggle with complex object movements and multi-object motion control, resulting in imprecise trajectory adherence, poor object consistency, and compromised visual quality. Furthermore, these methods only support trajectory control in a single format, limiting their applicability in diverse scenarios. Additionally, there is no publicly available dataset or benchmark specifically tailored for trajectory-controllable video generation, hindering robust training and systematic evaluation. To address these challenges, we introduce MagicMotion, a novel image-to-video generation framework that enables trajectory control through three levels of conditions from dense to sparse: masks, bounding boxes, and sparse boxes. Given an input image and trajectories, MagicMotion seamlessly animates objects along defined trajectories while maintaining object consistency and visual quality. Furthermore, we present MagicData, a large-scale trajectory-controlled video dataset, along with an automated pipeline for annotation and filtering. We also introduce MagicBench, a comprehensive benchmark that assesses both video quality and trajectory control accuracy across different numbers of objects. Extensive experiments demonstrate that MagicMotion outperforms previous methods across various metrics.

📣 Updates

• 2025/07/28 🔥🔥MagicData has been released here. Welcome to use our dataset!
• 2025/06/26 🔥🔥MagicMotion has been accepted by ICCV2025!🎉🎉🎉
• 2025/03/28 🔥🔥We released interactive demo with gradio for MagicMotion.
• 2025/03/27 MagicMotion can now perform inference on a single 4090 GPU (with less than 24GB of GPU memory).
• 2025/03/21 🔥🔥We released MagicMotion, including inference code and model weights.

📑 Table of Contents

• 💡 Abstract
• 📣 Updates
• 📑 Table of Contents
• ✅ TODO List
• 🐍 Installation
• 📦 Model Weights
  • Folder Structure
  • Download Links
• 🔄 Inference
  • Scripts
• 🖥️ Gradio Demo
• 🤝 Acknowledgements
• 📚 Contact

✅ TODO List

• Release our inference code and model weights
• Release gradio demo
• Release MagicData
• Release MagicBench and evaluation code
• Release our training code

🐍 Installation

# Clone this repository.
git clone https://github.com/quanhaol/MagicMotion
cd MagicMotion

# Install requirements
conda env create -n magicmotion --file environment.yml
conda activate magicmotion
pip install git+https://github.com/huggingface/diffusers

# Install Grounded_SAM2
cd trajectory_construction/Grounded_SAM2
pip install -e .
pip install --no-build-isolation -e grounding_dino

# Optional: For image editing
pip install git+https://github.com/huggingface/image_gen_aux

📦 Model Weights

Folder Structure

MagicMotion
└── ckpts
    ├── stage1
    │   ├── mask.pt
    ├── stage2
    │   └── box.pt
    │   └── box_perception_head.pt
    ├── stage3
    │   └── sparse_box.pt
    │   └── sparse_box_perception_head.pt

Download Links

pip install "huggingface_hub[hf_transfer]"
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download quanhaol/MagicMotion --local-dir ckpts

🔄 Inference

Inference requires only 23GB of GPU memory (tested on a single 24GB NVIDIA GeForce RTX 4090 GPU).
If you have sufficient GPU memory, you can modify magicmotion/inference.py to improve runtime performance:

# Optimized setting (for GPUs with sufficient memory)
pipe.to("cuda")
# pipe.enable_sequential_cpu_offload()

Note: Using the optimized setting can reduce runtime by up to 2x.

Scripts

# Demo inference script of each stage (Input Image & Trajectory already provided)
bash magicmotion/scripts/inference/inference_mask.sh
bash magicmotion/scripts/inference/inference_box.sh
bash magicmotion/scripts/inference/inference_sparse_box.sh

# You an also construct trajectory for each stage by yourself -- See MagicMotion/trajectory_construction for more details
python trajectory_construction/plan_mask.py
python trajectory_construction/plan_box.py
python trajectory_construction/plan_sparse_box.py

# Optional: Use FLUX to generate input image by text-to-image generation or image editing -- See MagicMotion/first_frame_generation for more details
python first_frame_generation/t2i_flux.py
python first_frame_generation/edit_image_flux.py

🖥️ Gradio Demo

Usage:

bash magicmotion/scripts/app/app.sh

🤝 Acknowledgements

We would like to express our gratitude to the following open-source projects that have been instrumental in the development of our project:

• CogVideo: An open source video generation framework by THUKEG.
• Open-Sora: An open source video generation framework by HPC-AI Tech.
• finetrainers: A Memory-optimized training library for diffusion models.

Special thanks to the contributors of these libraries for their hard work and dedication!

📚 Contact

If you have any suggestions or find our work helpful, feel free to contact us

Email: liqh24@m.fudan.edu.cn or zhenxingfd@gmail.com

If you find our work useful, please consider giving a star to this github repository and citing it:

@article{li2025magicmotion,
  title={MagicMotion: Controllable Video Generation with Dense-to-Sparse Trajectory Guidance},
  author={Li, Quanhao and Xing, Zhen and Wang, Rui and Zhang, Hui and Dai, Qi and Wu, Zuxuan},
  journal={arXiv preprint arXiv:2503.16421},
  year={2025}
}