---
task_categories:
- text-to-3d
- image-to-3d
- image-text-to-image
- any-to-any
arxiv: 2609.04196
tags:
- Camera
- 3D Vision
- Spatial AI
- Physical AI
- World Model
- Camera Parameter
- APTv2
- Generation

---

# APTv2-Camera

![camera map collage](analysis/APTv2-Camera.png)

Per-image camera parameter annotations for the **APTv2** dataset
(an animal pose estimation & tracking benchmark; 41,569 images across 9 shards), captioned by the [**Puffin-World**](https://huggingface.co/ACERobotics/Puffin-World) model. More captioned datasets are provided in our [**Puffin-16M**](https://kangliao929.github.io/projects/puffin-16m/) website.

The collage above visualizes the camera maps on sample images — each
pair shows the **up field** (green arrows: the projected gravity-up direction)
and the **latitude field** (colored contours: angle above/below the horizon).

## Format

One `.tar` per shard (`aptv2_0000.tar` … ), each containing one `.json` per
image whose name matches the source image stem.

Each JSON holds the predicted monocular camera parameters:

| Field | Meaning | Unit |
|-------|---------|------|
| `roll`  | camera roll        | radians |
| `pitch` | camera pitch       | radians |
| `vfov`  | vertical field-of-view | radians |
| `k1`    | radial distortion coefficient | – |
| `parse_ok` | whether the model output parsed within valid ranges | bool |

Example:

```json
{"roll": 0.0123, "pitch": -0.0871, "vfov": 0.4012, "k1": 0.0000, "parse_ok": true}
```

## Camera Parameter Distributions

Histograms of the predicted roll / pitch / vertical-FoV over the whole dataset
(proportion of valid samples per 10° bin; `parse_ok=False` excluded).

![aptv2 camera stats](analysis/aptv2_camera_stats.png)

| split | roll μ / med / σ | pitch μ / med / σ | FoV μ / med / σ |
|-------|------------------|-------------------|-----------------|
| all (41,349) | 0.2° / 0.0° / 3.5° | −5.1° / −3.7° / 11.2° | 24.2° / 22.9° / 6.0° |

- **Roll** is tightly peaked at 0° (σ ≈ 3.5°; footage shot level).
- **Pitch** is centered slightly negative (mild downward-looking tendency).
- **FoV** is notably narrow (median ≈ 23°, the tightest of our captioned sets) —
  consistent with telephoto wildlife/animal photography that frames distant
  subjects.

If you'd like a dataset with a more diverse and uniform distribution of camera parameters, please refer to our [Puffin-4M](https://huggingface.co/datasets/KangLiao/Puffin-4M) and [Puffin-16M](https://huggingface.co/datasets/KangLiao/Puffin-16M) datasets.

### Dataset Download
You can download the entire dataset using the following command:
```bash
hf download KangLiao/APTv2-Camera --repo-type dataset
```

### From Camera Parameters to Up and Latitude Fields

The released `(roll, pitch, vfov, k1)` annotations can be converted into the dense perspective-field representation used by Puffin-World. The conversion computes focal length from `vfov`, constructs a radial camera, and maps roll and pitch to the gravity direction. [`get_perspective_field`](https://github.com/KangLiao929/Puffin/blob/main/Puffin-World/scripts/camera/geometry/perspective_fields.py) then returns a normalized 2-channel **up field** and a 1-channel **latitude field**. The up field encodes the projected world-up direction at every pixel, while the latitude field measures each viewing ray's angular elevation relative to the horizon.

```python
import json
import torch
from scripts.camera.geometry.camera import SimpleRadial
from scripts.camera.geometry.gravity import Gravity
from scripts.camera.geometry.perspective_fields import get_perspective_field
from scripts.camera.utils.conversions import fov2focal

with open("camera.json") as f:
    annotation = json.load(f)
roll, pitch, vfov, k1 = (
    annotation[key] for key in ("roll", "pitch", "vfov", "k1")
)

H, W = 512, 512
f = float(fov2focal(torch.tensor(vfov), H))
camera = SimpleRadial(torch.tensor(
    [W, H, f, f, W / 2, H / 2, k1, 0.0]
).float()).scale(torch.tensor([1.0, 1.0]))
gravity = Gravity.from_rp(torch.tensor(roll), torch.tensor(pitch))
up_field, latitude_field = get_perspective_field(camera, gravity)
# Shapes: [1, 2, H, W] and [1, 1, H, W]
```

Run the example from the `Puffin-World` directory. Use [`plot_vector_fields` and `plot_latitudes`](https://github.com/KangLiao929/Puffin/blob/main/Puffin-World/scripts/camera/visualization/viz2d.py) to render up-field arrows and latitude heatmaps or contours. See [`save_pf_visualization`](https://github.com/KangLiao929/Puffin/blob/main/Puffin-World/scripts/demo/physics_perception.py) for an end-to-end visualization example.

### Caption Pipeline
Beyond this captioned dataset, we also release **a complete captioning pipeline** for annotating camera parameters for arbitrary datasets, analyzing camera parameter distributions, and visualizing the corresponding camera maps. The pipeline is available in our [GitHub repository](https://github.com/KangLiao929/Puffin/blob/main/Puffin-World/documents/ANNOTATION_CAMERA.md).


### Citation
If you find the captioned dataset useful for your research or applications, please cite the following papers using these BibTeX entries:

```bibtex
  @article{liao2025puffin,
    title={Thinking with Camera: A Unified Multimodal Model for Camera-Centric Understanding and Generation},
    author={Liao, Kang and Wu, Size and Wu, Zhonghua and Jin, Linyi and Wang, Chao and Wang, Yikai and Wang, Fei and Li, Wei and Loy, Chen Change},
    journal={arXiv preprint arXiv:2510.08673},
    year={2025}
  }

  @article{liao2026puffinworld,
    title={Puffin-World: Scaling a Unified Multimodal Model with Native 3D World States},
    author={Liao, Kang and Luo, Yihang and Wu, Xiao-Ming and Jin, Linyi and Wu, Size and Lin, Chunyu and Zhao, Yao and Wang, Fei and Li, Wei and Loy, Chen Change},
    journal={arXiv preprint arXiv:2609.04196},
    year={2026}
  }
```