116 lines
4.3 KiB
Markdown
116 lines
4.3 KiB
Markdown
# SpatialTrackerV3: 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/), [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/)
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### [Project Page]() | [BibTeX]() | [Goolge Drive]()
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<!-- [Paper V1]() | [Paper V2]() | [Paper V3]() | -->
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<a href="https://arxiv.org/abs/xxx">
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<img alt="Technical Report" src="https://img.shields.io/badge/Technical%20Report-arXiv:xxx" style="border: 1px solid black;">
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</a>
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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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<a href="">
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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 width="1100" src="./assets/teaser_1.png" />
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## TODO List
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1. **Release Offline Version**
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- [x] `SpaTrack3` + `Moge` ➔ supports `unposed RGB` as input.
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- [x] `SpaTrack3`+ `MegaSAM` ➔ supports `Posed RGBD` as input.
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- [x] `SpaTrack3` + `VGGT` ➔ make `VGGT` works in `Dynamic Scenes`.
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2. **Release Online Version**
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- [ ] Sliding windows version.
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3. **More Releases**
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- [ ] Some `Ceres Python Bindings` designed for SpatialTracker and Dynamic Reconstruction.
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- [ ] More supports for other Depth Model, *i.e.*, `DepthAnything`, `StereoFoundation`, `UniDepth`, `Metric3D`.
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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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git clone git@github.com:henry123-boy/SpaTrackerV3.git
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cd SpaTrackerV3
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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 SpaTrack3 python=3.11
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conda activate SpaTrack3
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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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4. **Install SpaTrack3 Visualizer:**
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```bash
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cd viser
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python -m pip install -e .
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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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## Download the Checkpoints
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```
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mkdir -p checkpoints
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```
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Step1: Download the checkpoint of `Moge` from [here](https://github.com/microsoft/MoGe), and put the `model.pt` into `./checkpoints/`
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Step2: Download the checkpoint of `SpaTrack3` from [GoolgeDrive](https://drive.google.com/drive/folders/1GYeC639gA23N_OiytGHXTUCSYrbM0pOo?usp=sharing), and place it into `./checkpoints/`
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## Quick Start
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We gave two examples to illustrate the usage of `SpaTrack3`. Firstly, please download `ckpts` and `examples` via:
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```
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sh scripts/download.sh
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```
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### Type1: Posed RGBD video *(Example0)*
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We provide an example who has Posed RGBD input with [MegaSAM](https://github.com/mega-sam/mega-sam).
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```
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python inference.py --data_type="RGBD" --data_dir="assets/example0" --video_name="snowboard" --fps=1
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```
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### Type2: unposed RGB video *(Example1)*
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```
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python inference.py --data_type="RGB" --data_dir="assets/example1" --video_name="xhs" --fps=6
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```
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### Visualize your results
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We provide two types of visualization. The 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,
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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 enable the user to track the points on the target object easily, just try:
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```
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python app.py
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```
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Here is an example with our GUI:
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