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.gitattributes

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+samples/
+installation.sh
+accelerate_configs
+magicmotion/dataset
+magicmotion/train.py
+huggingface_download.sh
+huggingface_uplaod.py

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README.md

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+<img src="assets/teaser2.webp" width="100%" alt="Teaser Image">
+<br>
+<a href="https://arxiv.org/pdf/2503.16421"><img src="https://img.shields.io/static/v1?label=Paper&message=2503.16421&color=red&logo=arxiv"></a>
+<a href="https://quanhaol.github.io/magicmotion-site/"><img src="https://img.shields.io/static/v1?label=Project&message=Page&color=green&logo=github-pages"></a>
+<a href="https://huggingface.co/quanhaol/MagicMotion"><img src="https://img.shields.io/badge/🤗_HuggingFace-Model-ffbd45.svg" alt="HuggingFace"></a>
+<a href="https://huggingface.co/datasets/quanhaol/MagicData"><img src="https://img.shields.io/badge/🤗_HuggingFace-Dataset-ffbd45.svg" alt="HuggingFace"></a>
+
+> **MagicMotion: Controllable Video Generation with Dense-to-Sparse Trajectory Guidance**
+> <br>
+> [Quanhao Li\*](https://github.com/quanhaol), [Zhen Xing\*](https://chenhsing.github.io/), [Rui Wang](https://scholar.google.com/citations?user=116smmsAAAAJ&hl=en), [Hui Zhang](https://huizhang0812.github.io/), [Qi Dai](https://daiqi1989.github.io/), and [Zuxuan Wu](https://zxwu.azurewebsites.net/)
+> <br>
+\* 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.
+
+
+<img src="assets/teaser.webp" width="100%" alt="Teaser Image">
+
+## 📣 Updates
+- `2025/07/28` 🔥🔥MagicData has been released [`here`](https://huggingface.co/datasets/quanhaol/MagicData). 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](#-abstract)
+- [📣 Updates](#-updates)
+- [📑 Table of Contents](#-table-of-contents)
+- [✅ TODO List](#-todo-list)
+- [🐍 Installation](#-installation)
+- [📦 Model Weights](#-model-weights)
+  - [Folder Structure](#folder-structure)
+  - [Download Links](#download-links)
+- [🔄 Inference](#-inference)
+  - [Scripts](#scripts)
+- [🖥️ Gradio Demo](#️-gradio-demo)
+- [🤝 Acknowledgements](#-acknowledgements)
+- [📚 Contact](#-contact)
+
+## ✅ TODO List
+
+- [x] Release our inference code and model weights
+- [x] Release gradio demo
+- [x] Release MagicData
+- [ ] Release MagicBench and evaluation code
+- [ ] Release our training code
+
+## 🐍 Installation
+
+```bash
+# 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
+
+```bash
+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:
+
+```python
+# 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
+```bash
+# 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
+bash magicmotion/scripts/app/app.sh
+```
+<img src="assets/images/gradio/1.png" alt="Gradio Demo 1" style="width: 60%; border: 1px solid #ddd; border-radius: 4px; padding: 5px;"> <img src="assets/images/gradio/2.png" alt="Gradio Demo 2" style="width: 60%; border: 1px solid #ddd; border-radius: 4px; padding: 5px;">
+
+## 🤝 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](https://github.com/THUDM/CogVideo): An open source video generation framework by THUKEG.
+- [Open-Sora](https://github.com/hpcaitech/Open-Sora): An open source video generation framework by HPC-AI Tech.
+- [finetrainers](https://github.com/a-r-r-o-w/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, <b>please consider giving a star to this github repository and citing it</b>:
+
+```bibtex
+@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}
+}
+```

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change_mask.py

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+import cv2
+import numpy as np
+import os
+
+# 路径配置
+input_path  = "/mnt/prev_nas/qhy/MagicMotion/assets/mask_trajectory/mammoth_rhino.mp4"
+output_path = "/mnt/prev_nas/qhy/MagicMotion/assets/mask_trajectory/mammoth_rhino_frozen_pink_bgr_range.mp4"
+
+# 你已经验证通过的两个参考颜色（BGR）
+pink_bgr = np.array([77, 80, 119], dtype=np.float32)  # #77504D
+red_bgr  = np.array([33, 37, 117], dtype=np.float32)  # #752521
+
+# 阈值（根据你单帧测试结果，目前用 30，如果想收紧可以再调小）
+T_pink = 30.0
+T_red  = 30.0
+
+def get_color_masks_bgr(frame_bgr):
+    """
+    输入: frame_bgr: uint8, HxWx3
+    输出: pink_mask, red_mask (bool 的 HxW)
+    使用 BGR 空间的欧式距离 + 阈值来划分粉色和红色。
+    """
+    img = frame_bgr.astype(np.float32)
+    pixels = img.reshape(-1, 3)
+
+    dist_to_pink = np.linalg.norm(pixels - pink_bgr, axis=1)
+    dist_to_red  = np.linalg.norm(pixels - red_bgr,  axis=1)
+
+    h, w = frame_bgr.shape[:2]
+    pink_mask = (dist_to_pink < T_pink).reshape(h, w)
+    red_mask  = (dist_to_red  < T_red ).reshape(h, w)
+
+    # 冲突点按“谁近归谁”分配
+    both = pink_mask & red_mask
+    if both.any():
+        idx_flat = np.where(both.ravel())[0]
+        closer_to_pink = dist_to_pink[idx_flat] <= dist_to_red[idx_flat]
+
+        both_y, both_x = np.where(both)
+        for i, (y, x) in enumerate(zip(both_y, both_x)):
+            if closer_to_pink[i]:
+                red_mask[y, x] = False
+            else:
+                pink_mask[y, x] = False
+
+    return pink_mask, red_mask
+
+def main():
+    cap = cv2.VideoCapture(input_path)
+    if not cap.isOpened():
+        raise RuntimeError(f"无法打开视频: {input_path}")
+
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    width  = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+    # 先用 mp4v 写 mp4，如果要 H.264 可以再用 ffmpeg 转
+    fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+    out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
+    if not out.isOpened():
+        cap.release()
+        raise RuntimeError("VideoWriter 打开失败")
+
+    # ---------- 第一帧：冻结粉色 ----------
+    ret, first_frame = cap.read()
+    if not ret:
+        cap.release()
+        out.release()
+        raise RuntimeError("无法读取第一帧")
+
+    pink_mask_first, _ = get_color_masks_bgr(first_frame)
+
+    frozen_pink = np.zeros_like(first_frame)
+    frozen_pink[pink_mask_first] = first_frame[pink_mask_first]
+
+    # 回到第一帧，从头处理整段视频
+    cap.set(cv2.CAP_PROP_POS_FRAMES, 0)
+
+    frame_idx = 0
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        _, red_mask = get_color_masks_bgr(frame)
+
+        # 当前帧红色部分
+        red_part = np.zeros_like(frame)
+        red_part[red_mask] = frame[red_mask]
+
+        # 背景黑 + 冻结粉色 + 当前红色
+        combined = np.zeros_like(frame)
+        combined += frozen_pink
+        combined += red_part
+
+        out.write(combined)
+        frame_idx += 1
+        if frame_idx % 50 == 0:
+            print(f"已处理帧数: {frame_idx}")
+
+    cap.release()
+    out.release()
+    print("处理完成，输出文件:", output_path)
+
+if __name__ == "__main__":
+    main()

environment.yml

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+name: magicmotion
+channels:
+  - defaults
+dependencies:
+  - _libgcc_mutex=0.1=main
+  - _openmp_mutex=5.1=1_gnu
+  - bzip2=1.0.8=h5eee18b_6
+  - ca-certificates=2024.7.2=h06a4308_0
+  - ld_impl_linux-64=2.38=h1181459_1
+  - libffi=3.4.4=h6a678d5_1
+  - libgcc-ng=11.2.0=h1234567_1
+  - libgomp=11.2.0=h1234567_1
+  - libstdcxx-ng=11.2.0=h1234567_1
+  - libuuid=1.41.5=h5eee18b_0
+  - ncurses=6.4=h6a678d5_0
+  - openssl=3.0.15=h5eee18b_0
+  - pip=24.2=py310h06a4308_0
+  - python=3.10.14=h955ad1f_1
+  - readline=8.2=h5eee18b_0
+  - setuptools=72.1.0=py310h06a4308_0
+  - sqlite=3.45.3=h5eee18b_0
+  - tk=8.6.14=h39e8969_0
+  - wheel=0.43.0=py310h06a4308_0
+  - xz=5.4.6=h5eee18b_1
+  - zlib=1.2.13=h5eee18b_1
+  - pip:
+      - --extra-index-url https://pypi.nvidia.com
+      - absl-py==2.1.0
+      - accelerate==1.5.2
+      - addict==2.4.0
+      - aiohappyeyeballs==2.4.0
+      - aiohttp==3.10.5
+      - aiosignal==1.3.1
+      - anyio==4.4.0
+      - argon2-cffi==23.1.0
+      - argon2-cffi-bindings==21.2.0
+      - arrow==1.3.0
+      - async-timeout==4.0.3
+      - attrs==24.2.0
+      - beautifulsoup4==4.12.3
+      - bitsandbytes==0.45.3
+      - bleach==6.1.0
+      - blobfile==3.0.0
+      - bokeh==3.4.3
+      - cachetools==5.5.0
+      - certifi==2024.8.30
+      - cffi==1.17.1
+      - charset-normalizer==3.3.2
+      - click==8.1.7
+      - cloudpickle==3.0.0
+      - colorcet==3.1.0
+      - contourpy==1.3.0
+      - cucim-cu12==24.8.0
+      - cuda-python==12.6.0
+      - cudf-cu12==24.8.2
+      - cugraph-cu12==24.8.0
+      - cuml-cu12==24.8.0
+      - cuproj-cu12==24.8.0
+      - cupy-cuda12x==13.3.0
+      - cuspatial-cu12==24.8.0
+      - cuvs-cu12==24.8.0
+      - cuxfilter-cu12==24.8.0
+      - cycler==0.12.1
+      - dask==2024.7.1
+      - dask-cuda==24.8.2
+      - dask-cudf-cu12==24.8.2
+      - dask-expr==1.1.9
+      - datasets==3.4.1
+      - datashader==0.16.3
+      - deepspeed==0.16.4
+      - defusedxml==0.7.1
+      - distributed==2024.7.1
+      - distributed-ucxx-cu12==0.39.1
+      - distro==1.9.0
+      - einops==0.8.1
+      - fairscale==0.4.13
+      - fastapi==0.114.0
+      - fastjsonschema==2.20.0
+      - fastrlock==0.8.2
+      - ffmpy==0.4.0
+      - fire==0.6.0
+      - fonttools==4.53.1
+      - fqdn==1.5.1
+      - frozenlist==1.4.1
+      - fsspec==2024.9.0
+      - gdown==5.2.0
+      - geopandas==1.0.1
+      - gitdb==4.0.11
+      - gitpython==3.1.43
+      - gradio==4.44.0
+      - gradio-client==1.3.0
+      - grpcio==1.66.1
+      - gurobipy==11.0.3
+      - h5py==3.11.0
+      - hf-transfer==0.1.9
+      - hickle==5.0.3
+      - holoviews==1.19.1
+      - httpcore==1.0.5
+      - httpx==0.27.2
+      - huggingface-hub==0.27.0
+      - hydra-core==1.3.2
+      - idna==3.8
+      - imageio==2.35.1
+      - importlib-metadata==8.5.0
+      - importlib-resources==6.4.5
+      - iopath==0.1.10
+      - isoduration==20.11.0
+      - jinja2==3.1.4
+      - jiter==0.5.0
+      - jmespath==1.0.1
+      - joblib==1.4.2
+      - jsonpointer==3.0.0
+      - jsonschema==4.23.0
+      - jsonschema-specifications==2023.12.1
+      - jupyter-client==8.6.2
+      - jupyter-core==5.7.2
+      - jupyter-events==0.10.0
+      - jupyter-server==2.14.2
+      - jupyter-server-proxy==4.4.0
+      - jupyter-server-terminals==0.5.3
+      - jupyterlab-pygments==0.3.0
+      - kiwisolver==1.4.7
+      - kmeans-pytorch==0.3
+      - lazy-loader==0.4
+      - libucx-cu12==1.15.0.post1
+      - linkify-it-py==2.0.3
+      - llama-models==0.0.13
+      - llama-toolchain==0.0.13
+      - llvmlite==0.43.0
+      - locket==1.0.0
+      - lxml==5.3.0
+      - markdown==3.7
+      - markupsafe==2.1.5
+      - matplotlib==3.9.2
+      - mdit-py-plugins==0.4.2
+      - mistune==3.0.2
+      - multipledispatch==1.0.0
+      - nbclient==0.10.0
+      - nbconvert==7.16.4
+      - nbformat==5.10.4
+      - networkx==3.3
+      - numba==0.60.0
+      - numpy==1.26.4
+      - nvidia-cublas-cu12==12.1.3.1
+      - nvidia-cuda-cupti-cu12==12.1.105
+      - nvidia-cuda-nvrtc-cu12==12.1.105
+      - nvidia-cuda-runtime-cu12==12.1.105
+      - nvidia-cudnn-cu12==9.1.0.70
+      - nvidia-cufft-cu12==11.0.2.54
+      - nvidia-curand-cu12==10.3.2.106
+      - nvidia-cusolver-cu12==11.4.5.107
+      - nvidia-cusparse-cu12==12.1.0.106
+      - nvidia-ml-py==12.570.86
+      - nvidia-nccl-cu12==2.20.5
+      - nvidia-nvjitlink-cu12==12.6.68
+      - nvidia-nvtx-cu12==12.1.105
+      - nvtx==0.2.10
+      - openai==1.44.1
+      - opencv-python-headless==4.10.0.84
+      - orjson==3.10.7
+      - overrides==7.7.0
+      - pandas==2.2.2
+      - pandocfilters==1.5.1
+      - panel==1.4.5
+      - param==2.1.1
+      - partd==1.4.2
+      - peft==0.14.0
+      - platformdirs==4.3.2
+      - portalocker==2.10.1
+      - prometheus-client==0.20.0
+      - protobuf==5.28.0
+      - psutil==6.0.0
+      - ptyprocess==0.7.0
+      - pulp==2.9.0
+      - pyarrow==16.1.0
+      - pycocotools==2.0.8
+      - pycparser==2.22
+      - pycryptodomex==3.20.0
+      - pyct==0.5.0
+      - pydantic==2.9.1
+      - pydantic-core==2.23.3
+      - pygments==2.18.0
+      - pylibcugraph-cu12==24.8.0
+      - pylibraft-cu12==24.8.1
+      - pynvjitlink-cu12==0.3.0
+      - pynvml==11.4.1
+      - pyogrio==0.9.0
+      - pyparsing==3.1.4
+      - pyproj==3.6.1
+      - pysocks==1.7.1
+      - python-json-logger==2.0.7
+      - python-multipart==0.0.9
+      - pytz==2024.2
+      - pyviz-comms==3.0.3
+      - pyyaml==6.0.2
+      - pyzmq==26.2.0
+      - raft-dask-cu12==24.8.1
+      - rapids-dask-dependency==24.8.0
+      - referencing==0.35.1
+      - rfc3339-validator==0.1.4
+      - rfc3986-validator==0.1.1
+      - rich==13.8.1
+      - rmm-cu12==24.8.2
+      - rpds-py==0.20.0
+      - ruff==0.6.4
+      - safetensors==0.4.5
+      - scikit-image==0.23.2
+      - scikit-learn==1.5.1
+      - scipy==1.14.1
+      - send2trash==1.8.3
+      - sentencepiece==0.2.0
+      - shapely==2.0.6
+      - simpervisor==1.0.0
+      - six==1.16.0
+      - smmap==5.0.1
+      - sortedcontainers==2.4.0
+      - soupsieve==2.6
+      - starlette==0.38.5
+      - supervision==0.23.0
+      - sympy==1.13.2
+      - tblib==3.0.0
+      - tensorboard==2.17.1
+      - tensorboard-data-server==0.7.2
+      - termcolor==2.4.0
+      - terminado==0.18.1
+      - threadpoolctl==3.5.0
+      - tifffile==2024.8.30
+      - tiktoken==0.7.0
+      - timm==1.0.9
+      - tinycss2==1.3.0
+      - tokenizers==0.19.1
+      - tomli==2.0.1
+      - toolz==0.12.1
+      - torch==2.4.1
+      - torchao==0.9.0
+      - torchaudio==2.4.1
+      - torchdata==0.11.0
+      - torchvision==0.19.1
+      - tornado==6.4.1
+      - traitlets==5.14.3
+      - transformers==4.44.2
+      - treelite==4.3.0
+      - triton==3.0.0
+      - typer==0.12.5
+      - types-python-dateutil==2.9.0.20240906
+      - tzdata==2024.1
+      - uc-micro-py==1.0.3
+      - ucx-py-cu12==0.39.2
+      - ucxx-cu12==0.39.1
+      - uri-template==1.3.0
+      - urllib3==2.2.2
+      - uvicorn==0.30.6
+      - webcolors==24.8.0
+      - webencodings==0.5.1
+      - websocket-client==1.8.0
+      - websockets==12.0
+      - werkzeug==3.0.4
+      - xarray==2024.9.0
+      - xyzservices==2024.9.0
+      - yapf==0.40.2
+      - yarl==1.11.1
+      - zict==3.0.0
+      - zipp==3.20.1
+      - imageio-ffmpeg==0.6.0