# SpatialTrackerV3: 3D Point Tracking Made Easy

**[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/)**

[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/), [Xin 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/)

### [Project Page]() | [BibTeX]() | [Goolge Drive]() 

<!-- [Paper V1]() | [Paper V2]() | [Paper V3]() | -->

<a href="https://arxiv.org/abs/xxx">
  <img alt="Technical Report" src="https://img.shields.io/badge/Technical%20Report-arXiv:xxx" style="border: 1px solid black;">
</a>
<a target="_blank" href="">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
<a href="">
  <img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue">
</a>


<img width="1100" src="./assets/teaser_1.png" />

## TODO List

1. **Release Offline Version**
   - [x] `SpaTrack3` + `Moge` ➔ supports `unposed RGB` as input.
   - [x] `SpaTrack3`+ `MegaSAM` ➔ supports `Posed RGBD` as input. 
   - [x] `SpaTrack3` + `VGGT` ➔ make `VGGT` works in `Dynamic Scenes`.

2. **Release Online Version**
   - [ ] Sliding windows version.

3. **More Releases**
   - [ ] Some `Ceres Python Bindings` designed for SpatialTracker and Dynamic Reconstruction.
   - [ ] More supports for other Depth Model, *i.e.*, `DepthAnything`, `StereoFoundation`, `UniDepth`, `Metric3D`.

## Set up the environment
To set up the environment for running the SpaTrack model, follow these steps:

1. **Clone the Repository:**
   ```bash
   git clone git@github.com:henry123-boy/SpaTrackerV3.git
   cd SpaTrackerV3
   ```

2. **Create a Virtual Environment:**
   It's recommended to use a virtual environment to manage dependencies.
   ```bash
   conda create -n SpaTrack3 python=3.11
   conda activate SpaTrack3
   ```

3. **Install Dependencies:**

   Install the torch dependencies `pip` (tested with `torch2.4`).
   ```bash
   python -m pip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 --index-url https://download.pytorch.org/whl/cu124
   ```

   Install the required Python packages using `pip`.
   ```bash
   python -m pip install -r requirements.txt
   ```

4. **Install SpaTrack3 Visualizer:**
   
   ```bash
   cd viser
   python -m pip install -e .
   ```
By following these steps, you should have a working environment ready to run the SpaTrack model.

## Download the Checkpoints

```
mkdir -p checkpoints
```
Step1: Download the checkpoint of `Moge` from [here](https://github.com/microsoft/MoGe), and put the `model.pt` into `./checkpoints/`

Step2: Download the checkpoint of `SpaTrack3` from [GoolgeDrive](https://drive.google.com/drive/folders/1GYeC639gA23N_OiytGHXTUCSYrbM0pOo?usp=sharing), and place it into `./checkpoints/`



## Quick Start
We gave two examples to illustrate the usage of `SpaTrack3`. Firstly, please download `ckpts` and `examples` via:
```
sh scripts/download.sh
```   
### Type1: Posed RGBD video *(Example0)*
We provide an example who has Posed RGBD input with [MegaSAM](https://github.com/mega-sam/mega-sam). 
```
python inference.py --data_type="RGBD" --data_dir="assets/example0" --video_name="snowboard" --fps=1
```  
### Type2: unposed RGB video *(Example1)*
```
python inference.py --data_type="RGB" --data_dir="assets/example1" --video_name="xhs" --fps=6
```

### Visualize your results
We provide two types of visualization. The guidance will be displayed in the terminal after running `inference.py`

## 🌟 Recommended: Gradio Demo with SAM 🌟
Please follow the instructions in the [app_3rd README](app_3rd/README.md) to configure the dependencies. Then, 
```
python -m pip install gradio==5.31.0 pako
```
Our gradio demo enable the user to track the points on the target object easily, just try:
```
python app.py
```
Here is an example with our GUI: 
![Demo](./assets/gradio.gif)