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READEME.md

@@ -0,0 +1,159 @@
+# MotionPro
+
+<p align="center">
+    <img src="assets/logo.png" width="400"/>
+<p>
+
+<p align="center">
+    🖥️ <a href="https://github.com/HiDream-ai/MotionPro">GitHub</a> &nbsp&nbsp ｜ &nbsp&nbsp  🌐 <a href="https://zhw-zhang.github.io/MotionPro-page/"><b>Project Page</b></a> &nbsp&nbsp  | &nbsp&nbsp🤗 <a href="https://huggingface.co/zzwustc/MotionPro/tree/main">Hugging Face</a>&nbsp&nbsp | &nbsp&nbsp 📑 <a href="">Paper </a> &nbsp&nbsp | &nbsp&nbsp 📖 <a href="">PDF</a> &nbsp&nbsp 
+<br>
+
+[**MotionPro: A Precise Motion Controller for Image-to-Video Generation**](https://zhw-zhang.github.io/MotionPro-page/) <be>
+
+🔆 If you find MotionPro useful, please give a ⭐ for this repo, which is important to Open-Source projects. Thanks!
+
+In this repository, we introduce **MotionPro**, an image-to-video generation model built on SVD. MotionPro learns object and camera motion control from **in-the-wild** video datasets (e.g., WebVid-10M) without applying special data filtering. The model offers the following key features:
+
+-  **User-friendly interaction.** Our model requires only simple conditional inputs, allowing users to achieve I2V motion control generation through brushing and dragging.
+-  **Simultaneous control of object and camera motion.** Our trained MotionPro model supports simultaneous object and camera motion control. Moreover, our model can achieve precise camera control driven by pose without requiring training on a specific camera-pose paired dataset. [More Details](assets/camera_control.png)
+-  **Synchronized video generation.** This is an extension of our model. By combining MotionPro and MotionPro-Dense, we can achieve synchronized video generation. [More Details](assets/README_syn.md)
+
+
+Additionally, our repository provides more tools to benefit the research community's development.:
+
+-  **Memory optimization for training.** We provide a training framework based on PyTorch Lightning, optimized for memory efficiency, enabling SVD fine-tuning with a batch size of 8 per NVIDIA A100 GPU.
+-  **Data construction tools.**  We offer scripts for constructing training data. Additionally, we also provide code for loading datasets in two formats, supporting video input from both folders (Dataset) and tar files (WebDataset).
+-  **MC-Bench and evaluation code.** We constructed MC-Bench with 1.1K user-annotated image-trajectory pairs, along with evaluation scripts for comprehensive assessments. All the images showcased on the project page can be found here.
+
+## Video Demos
+
+<div align="center">
+  <video src="assets/func_1.mp4" width="70%" autoplay loop muted playsinline poster="">
+  </video>
+  <p><em>Examples of different motion control types by our MotionPro.</em></p>
+</div>
+
+<!-- <div align="center">
+  <video src="assets/func_1.mp4" width="70%" autoplay loop muted playsinline poster="">
+  </video>
+  <p><em>Figure 2: Synchronized video generation and Video recapture.</em></p>
+</div> -->
+
+## 🔥 Updates
+- [x] **\[2025.03.26\]** Release inference and training code.
+- [ ] **\[2025.03.27\]** Upload gradio demo usage video.
+- [ ] **\[2025.03.29\]** Release MC-Bench and evaluation code.
+- [ ] **\[2025.03.30\]** Upload annotation tool for image-trajectory pair construction.
+
+## 🏃🏼 Inference
+<details open>
+<summary><strong>Environment Requirement</strong></summary>
+
+Clone the repo:
+```
+git clone https://github.com/HiDream-ai/MotionPro.git
+```
+
+Install dependencies:
+```
+conda create -n motionpro python=3.10.0
+conda activate motionpro
+pip install -r requirements.txt
+```
+</details>
+
+<details open>
+<summary><strong>Model Download</strong></summary>
+
+
+| Models            | Download Link                                                                 | Notes                                      |
+|-------------------|-------------------------------------------------------------------------------|--------------------------------------------|
+| MotionPro  | 🤗[Huggingface](https://huggingface.co/zzwustc/MotionPro/blob/main/MotionPro-gs_16k.pt)                | Supports both object and camera control. This is the default model mentioned in the paper.   |
+| MotionPro-Dense   | 🤗[Huggingface](https://huggingface.co/zzwustc/MotionPro/blob/main/MotionPro_Dense-gs_14k.pt)           | Supports synchronized video generation when combined with MotionPro. MotionPro-Dense shares the same architecture as Motion, but the input conditions are modified to include: dense optical flow and per-frame visibility masks relative to the first frame. |
+
+
+Download the model from HuggingFace at high speeds (30-80MB/s):
+```
+cd tools/huggingface_down
+bash download_hfd.sh
+```
+</details>
+
+
+<details open>
+<summary><strong>Run Motion Control</strong></summary>
+
+This section of the code supports simultaneous object motion and camera motion control. We provide a user-friendly Gradio demo interface that allows users to control motion with simple brushing and dragging operations. The instructional video can be found in `assets/demo.mp4` (please note the version of gradio).
+
+```
+python demo_sparse_flex_wh.py
+```
+When you expect all pixels to move (e.g., for camera control), you need to use the brush to fully cover the entire area. You can also test the demo using `assets/logo.png`.
+
+Additionally, users can also generate controllable image-to-video results using pre-defined camera trajectories. Note that our model has not been trained on a specific camera control dataset. Test the demo using `assets/sea.png`.
+
+```
+python demo_sparse_flex_wh_pure_camera.py
+```
+</details>
+
+
+<details open>
+<summary><strong>Run synchronized video generation and video recapture</strong></summary>
+
+By combining MotionPro and MotionPro-Dense, we can achieve the following functionalities:
+- Synchronized video generation. We assume that two videos, `pure_obj_motion.mp4` and `pure_camera_motion.mp4`, have been generated using the respective demos. By combining their motion flows and using the result as a condition for MotionPro-Dense, we obtain `final_video`. By pairing the same object motion with different camera motions, we can generate `synchronized videos` where the object motion remains consistent while the camera motion varies. [More Details](assets/README_syn.md)
+
+Here, you need to first download the [model_weights](https://huggingface.co/zzwustc/MotionPro/tree/main/tools/co-tracker/checkpoints) of cotracker and place them in the `tools/co-tracker/checkpoints` directory.
+
+```
+python inference_dense.py --ori_video 'assets/cases/dog_pure_obj_motion.mp4' --camera_video 'assets/cases/dog_pure_camera_motion_1.mp4' --save_name 'syn_video.mp4' --ckpt_path 'MotionPro-Dense CKPT-PATH'
+```
+
+</details>
+
+## 🚀 Training
+
+<details open>
+<summary><strong>Data Prepare</strong></summary>
+
+We have packaged several demo videos to help users debug the training code. Simply 🤗[download](https://huggingface.co/zzwustc/MotionPro/tree/main/data), extract the files, and place them in the `./data` directory.
+
+Additionally, `./data/dot_single_video` contains code for processing raw videos using [DOT](https://github.com/16lemoing/dot) to generate the necessary conditions for training, making it easier for the community to create training datasets.
+
+</details>
+
+
+<details open>
+<summary><strong>Train</strong></summary>
+
+Simply run the following command to train MotionPro:
+```
+train_server_1.sh
+```
+In addition to loading video data from folders, we also support [WebDataset](https://rom1504.github.io/webdataset/), allowing videos to be read directly from tar files for training. This can be enabled by modifying the config file:
+```
+train_debug_from_folder.yaml -> train_debug_from_tar.yaml 
+```
+
+Furthermore, to train the **MotionPro-Dense** model, simply modify the `train_debug_from_tar.yaml` file by changing `VidTar` to `VidTar_all_flow` and updating the `ckpt_path`.
+
+</details>
+
+## 🌟 Star and Citation
+If you find our work helpful for your research, please consider giving a star⭐ on this repository and citing our work📝.
+```
+@inproceedings{2025motionpro,
+  title={MotionPro: A Precise Motion Controller for Image-to-Video Generation},
+  author={Zhongwei Zhang, Fuchen Long, Zhaofan Qiu, Yingwei Pan, Wu Liu, Ting Yao and Tao Mei},
+  booktitle={CVPR},
+  year={2025}
+}
+```
+
+
+## 💖 Acknowledgement
+<span id="acknowledgement"></span>
+
+Our code is inspired by several works, including [SVD](https://github.com/Stability-AI/generative-models), [DragNUWA](https://github.com/ProjectNUWA/DragNUWA), [DOT](https://github.com/16lemoing/dot), [Cotracker](https://github.com/facebookresearch/co-tracker). Thanks to all the contributors! 
+

assets/README_syn.md

@@ -0,0 +1,38 @@
+## MotionPro-Dense Video Generation Pipeline
+
+
+This document provides an introduction to the MotionPro-Dense video generation pipeline, detailing its functionality and workflow. The pipeline is illustrated in the diagram below.
+
+### Pipeline Description
+
+1. **Video Generation with Base Motion Control**
+   - First, MotionPro can be used to generate videos with controllable object motion and camera motion.
+   
+2. **Optical Flow and Visibility Mask Extraction and Merging**  
+   - The generated videos are processed using CoTracker, a tool for extracting optical flow and visibility masks for each frame.  
+   - The extracted optical flows are accumulated through summation.  
+   - The per-frame visibility masks from both sequences are intersected to obtain the final visibility mask.
+
+3. **Final Video Generation with Combined Motions**  
+   - The aggregated motion conditions are used as input for **MotionPro-Dense**, which generates the final video with seamlessly integrated object and camera motions. 
+
+
+<div align="center">
+  <video src="./syn_video_control1.mp4" width="90%" autoplay loop muted playsinline poster="">
+  </video>
+  <p><em>Figure 1: Illustration of video generation with combined motions.</em></p>
+</div>
+
+
+### Synchronized Video Generation
+
+Additionally, the pipeline enables the generation of **synchronized videos**, where a consistent object motion is paired with different camera motions. 
+
+<div align="center">
+  <video src="./syn_video_control2.mp4" width="90%" autoplay loop muted playsinline poster="">
+  </video>
+  <p><em>Figure 2: Illustration of synchronized video generation.</em></p>
+</div>
+
+
+

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