117 lines
4.8 KiB
Markdown
117 lines
4.8 KiB
Markdown
# SpatialTrackerV2: 3D Point Tracking Made Easy
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**[CAD&CG, Zhejiang University](https://github.com/zju3dv)**; **[University of Oxford](https://www.robots.ox.ac.uk/~vgg/)**; **[Ant Research](https://www.antresearch.com/)**; **[Pixelwise AI](http://pixelwise.ai/)**; **[Bytedance Seed](https://seed.bytedance.com/zh/)**
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[Yuxi Xiao](https://henry123-boy.github.io/), [Jianyuan Wang](https://jytime.github.io/), [Nan Xue](https://xuenan.net/), [Nikita Karaev](https://nikitakaraevv.github.io/), [Iurii Makarov](https://linkedin.com/in/lvoursl), [Bingyi Kang](https://bingykang.github.io/), [Xing Zhu](https://openreview.net/profile?id=~Xing_Zhu2), [Hujun Bao](http://www.cad.zju.edu.cn/home/bao/), [Yujun Shen](https://shenyujun.github.io/), [Xiaowei Zhou](https://www.xzhou.me/)
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```bibtex
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@inproceedings{xiao2025spatialtrackerv2,
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title={SpatialTrackerV2: 3D Point Tracking Made Easy},
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author={Yuxi Xiao and Jianyuan Wang and Nan Xue and Nikita Karaev and Yuri Makarov and Bingyi Kang and Xing Zhu and Hujun Bao and Yujun Shen and Xiaowei Zhou},
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year={2025},
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booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
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url={https://arxiv.org/abs/2507.12462},
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}
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```
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### [Project Page](https://spatialtracker.github.io/) | [BibTeX]() | [Google Drive](https://drive.google.com/drive/u/1/folders/1GYeC639gA23N_OiytGHXTUCSYrbM0pOo?dmr=1&ec=wgc-drive-globalnav-goto)
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<!-- <a target="_blank" href="">
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<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
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</a>
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-->
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<a href="https://arxiv.org/abs/2507.12462"><img src="https://img.shields.io/badge/arXiv-2507.12462-b31b1b" alt="arXiv"></a>
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<a href="https://huggingface.co/spaces/Yuxihenry/SpatialTrackerV2">
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<img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue">
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</a>
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<img alt="Visitors" src="https://visitor-badge.laobi.icu/badge?page_id=henry123-boy.SpaTrackerV2&left_color=green&right_color=red">
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<img width="1100" src="./assets/teaser_1.png" />
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## 📰 Latest Updates & News
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- **[July 17, 2025]**: Our paper is on [arXiv](https://arxiv.org/abs/2507.12462)
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- **[June 27, 2025]**: SpatialTrackerV2 accepted by ICCV 2025
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- **[June 23, 2025]**: Huggingface Space Demo launched! Try it out: 🤗 [Huggingface Space](https://huggingface.co/spaces/Yuxihenry/SpatialTrackerV2)
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## TODO List
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- [x] Release quick start of `SpaTrack2-offline`
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- [x] Final version of Paper at [PAPER.md](./docs/PAPER.md)
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- [ ] Release `SpaTrack2-online`
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- [ ] Training & Evaluation Codes.
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- [ ] More supports for other Depth Model, *e.g.*, `DepthAnything`, `StereoFoundation`, `UniDepth`, `Metric3D`.
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- [ ] `Ceres Python Bindings` designed for SpatialTracker and Dynamic Reconstruction.
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## Set up the environment
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To set up the environment for running the SpaTrack model, follow these steps:
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1. **Clone the Repository:**
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```bash
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# clone the code
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git clone https://github.com/henry123-boy/SpaTrackerV2.git
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cd SpaTrackerV2
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# optionally download the example data to run the examples
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# Note: This will slowdown the clonining process, as it includes large files.
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git submodule update --init --recursive
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```
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2. **Create a Virtual Environment:**
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It's recommended to use a virtual environment to manage dependencies.
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```bash
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conda create -n SpaTrack2 python=3.11
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conda activate SpaTrack2
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```
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3. **Install Dependencies:**
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Install the torch dependencies `pip` (tested with `torch2.4`).
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```bash
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python -m pip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 --index-url https://download.pytorch.org/whl/cu124
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```
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Install the required Python packages using `pip`.
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```bash
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python -m pip install -r requirements.txt
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```
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By following these steps, you should have a working environment ready to run the SpaTrack model.
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## Quick Start
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Here are two examples of how to use `SpaTrack2`.
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### Type1: Monocular video as input *(Example0)*
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```
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python inference.py --data_type="RGB" --data_dir="examples" --video_name="protein" --fps=3
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```
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### Type2: RGBD video + Camera poses as input *(Example1)*
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We provide an example with Depth and Camera poses from [MegaSAM](https://github.com/mega-sam/mega-sam).
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First, download the `examples` via:
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```
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sh scripts/download.sh
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```
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Then run inference with the command with below:
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```
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python inference.py --data_type="RGBD" --data_dir="assets/example1" --video_name="snowboard" --fps=1
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```
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### Visualize your results
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Guidance will be displayed in the terminal after running `inference.py`.
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## 🌟 Recommended: Gradio Demo with SAM 🌟
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Please follow the instructions in the [app_3rd README](app_3rd/README.md) to configure the dependencies. Then, install the required packages:
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```
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python -m pip install gradio==5.31.0 pako
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```
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Our Gradio demo enables users to easily track points on the target object. Just try:
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```
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python app.py
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```
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