SpatialTracker: Tracking Any 2D Pixels in 3D Space

SpatialTracker: Tracking Any 2D Pixels in 3D Space,
Yuxi Xiao*, Qianqian Wang*, Shangzhan Zhang, Nan Xue, Sida Peng, Yujun Shen, Xiaowei Zhou,
CVPR 2024, Highlight Paper at arxiv

News and ToDo

• Release SpatialTracker-v2 (coming).
• Release SpatialTracker inference code and checkpoints.
• 05.04.2024: SpatialTracker is selected as Highlight Paper!
• 26.02.2024: SpatialTracker is accepted at CVPR 2024!

Requirements

The inference code was tested on

• Ubuntu 20.04
• Python 3.10
• PyTorch 2.1.1
• 1 NVIDIA GPU (RTX A6000) with CUDA version 11.8. (Other GPUs are also suitable, and 22GB GPU memory is sufficient for dense tracking (~10k points) with our code.)

Setup an environment

conda create -n SpaTrack python==3.10
conda activate SpaTrack

Install PyTorch

pip install torch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 --index-url https://download.pytorch.org/whl/cu118

Other Dependencies

pip install -r requirements.txt

Note: Please follow the version of the dependencies in requirements.txt to avoid potential conflicts.

Depth Estimator

In our default setting, monocular depth estimator is needed to acquire the metric depths from video input. There are several models for options (ZoeDepth, Metric3D, UniDepth and DepthAnything). We take ZoeDepth as default model. Download dpt_beit_large_384.pt, ZoeD_M12_K.pt, ZoeD_M12_NK.pt into models/monoD/zoeDepth/ckpts.

Data

Our method supports RGB or RGBD videos input. We provide the checkpoints and example_data at the Goolge Drive. Please download the spaT_final.pth and put it into ./checkpoints/.

RGB Videos

For example_data, we provide the butterfly.mp4 and butterfly_mask.png as an example. Download the butterfly.mp4 and butterfly_mask.png into ./assets/. And run the following command:

python demo.py --model spatracker --downsample 1 --vid_name butterfly --len_track 1 --fps_vis 15  --fps 1 --grid_size 40 --gpu ${GPU_id}

RGBD Videos

we provide the sintel_bandage.mp4, sintel_bandage.png and sintel_bandage/ in example_data. sintel_bandage/ includes the depth map of the sintel_bandage.mp4. Download the sintel_bandage.mp4, sintel_bandage.png and sintel_bandage/ into ./assets/. And run the following command:

python demo.py --model spatracker --downsample 1 --vid_name sintel_bandage --len_track 1 --fps_vis 15  --fps 1 --grid_size 60 --gpu ${GPU_id} --point_size 1 --rgbd # --vis_support (optional to visualize all the points)

Visualization 3D Trajectories

Firstly, please make sure that you have installed blender. We provide the visualization code for blender:

/Applications/Blender.app/Contents/MacOS/Blender -P create.py -- --input ./vis_results/sintel_bandage_3d.npy

For example, the sintel_bandage looked like

Citation

If you find our work useful in your research, please consider citing:

@inproceedings{SpatialTracker,
    title={SpatialTracker: Tracking Any 2D Pixels in 3D Space},
    author={Xiao, Yuxi and Wang, Qianqian and Zhang, Shangzhan and Xue, Nan and Peng, Sida and Shen, Yujun and Zhou, Xiaowei},
    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    year={2024}
}

Acknowledgement

Spatialtracker is built on top of Cotracker codebase. We appreciate the authors for their greate work and follow the License of Cotracker.