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pipeline_tag: depth-estimation
library_name: torch
license: mit
---
# TAPIP3D: Tracking Any Point in Persistent 3D Geometry
This repository contains the TAPIP3D model as presented in [TAPIP3D: Tracking Any Point in Persistent 3D Geometry](https://huggingface.co/papers/2504.14717).
[Project Page](https://tapip3d.github.io)
Code: https://github.com/zbww/tapip3d
**Overview**
TAPIP3D is a method for long-term feed-forward 3D point tracking in monocular RGB and RGB-D video sequences. It introduces a 3D feature cloud representation that lifts image features into a persistent world coordinate space, canceling out camera motion and enabling accurate trajectory estimation across frames.
## Demo Usage
We provide a simple demo script `inference.py`, along with sample input data located in the `demo_inputs/` directory. The script accepts as input either an `.mp4` video file or an `.npz` file. If providing an `.npz` file, it should follow the following format:
- `video`: array of shape (T, H, W, 3), dtype: uint8
- `depths` (optional): array of shape (T, H, W), dtype: float32
- `intrinsics` (optional): array of shape (T, 3, 3), dtype: float32
- `extrinsics` (optional): array of shape (T, 4, 4), dtype: float32
For demonstration purposes, the script uses a 32x32 grid of points at the first frame as queries.
### Inference with Monocular Video
By providing a video as `--input_path`, the script first runs [MegaSAM](https://github.com/mega-sam/mega-sam) with [MoGe](https://wangrc.site/MoGePage/) to estimate depth maps and camera parameters. Subsequently, the model will process these inputs within the global frame.
To run inference:
```bash
python inference.py --input_path demo_inputs/sheep.mp4 --checkpoint checkpoints/tapip3d_final.pth --resolution_factor 2
```
An npz file will be saved to `outputs/inference/`. To visualize the results:
```bash
python visualize.py <result_npz_path>
```
### Inference with Known Depths and Camera Parameters
If an `.npz` file containing all four keys (`rgb`, `depths`, `intrinsics`, `extrinsics`) is provided, the model will operate in an aligned global frame, generating point trajectories in world coordinates.
## Citation
If you find this project useful, please consider citing:
```
@article{tapip3d,
title={TAPIP3D: Tracking Any Point in Persistent 3D Geometry},
author={Zhang, Bowei and Ke, Lei and Harley, Adam W and Fragkiadaki, Katerina},
journal={arXiv preprint arXiv:2504.14717},
year={2025}
}
``` |