File size: 1,683 Bytes
f791b7a
 
 
 
 
 
 
 
4483f59
 
 
 
 
 
 
 
 
 
 
 
 
 
b2af22b
4483f59
 
 
 
 
 
b2af22b
4483f59
 
 
 
 
3435ee5
4483f59
 
b2af22b
4483f59
 
 
 
 
 
 
 
3435ee5
4483f59
 
 
 
3435ee5
4483f59
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
---
library_name: dust3r
tags:
- image-to-3d
- model_hub_mixin
- pytorch_model_hub_mixin
---


# St4RTrack: Simultaneous 4D Reconstruction and Tracking in the World

```bibtex
@inproceedings{st4rtrack2025,
  title={St4RTrack: Simultaneous 4D Reconstruction and Tracking in the World},
  author={Feng*, Haiwen and Zhang*, Junyi and Wang, Qianqian and Ye, Yufei and Yu, Pengcheng and Black, Michael J. and Darrell, Trevor and Kanazawa, Angjoo},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year={2025}
}
```

## Model info

Github page: https://github.com/HavenFeng/St4RTrack  
Project page: https://st4rtrack.github.io/  
Paper: https://arxiv.org/abs/2504.13152  


## How to Use

First install [St4rTrack](https://github.com/HavenFeng/St4RTrack). To load the model (Seq):

```python
from dust3r.models import AsymmetricCroCo3DStereo

# Loads default Seq checkpoint
model = AsymmetricCroCo3DStereo.from_pretrained("yupengchengg147/St4RTrack")
```

Run the following code to load the checkpoint trained with Pair Mode:

```python
from huggingface_hub import hf_hub_download
import tempfile
import os
from dust3r.model import AsymmetricCroCo3DStereo

# Create a temporary directory for the model files
temp_dir = os.path.join(tempfile.gettempdir(), "St4RTrack_pair")
os.makedirs(temp_dir, exist_ok=True)

# Download the config and model files from the Pair subfolder
config_path = hf_hub_download(
    repo_id="yupengchengg147/St4RTrack",
    filename="Pair/config.json",
    cache_dir=temp_dir
)
# Load the model from the downloaded path
model_dir = os.path.dirname(config_path)
model = AsymmetricCroCo3DStereo.from_pretrained(model_dir)
```