---
task_categories:
- text-to-3d
- image-to-3d
- image-text-to-image
- any-to-any
tags:
- Camera
- 3D Vision
- Spatial AI
- Physical AI
- World Model
- Camera Parameter
- DL3DV
- Generation
---

# DL3DV-Absolute-Camera

![camera map collage](analysis/DL3DV-Absolute-Camera.png)

Per-frame **camera parameter** annotations for the **DL3DV** dataset
(**6,377 scenes** across 7 buckets `1K`–`7K`; **2,161,003** valid per-frame
annotations), captioned by the [**Puffin-World**](https://github.com/KangLiao929/Puffin) model.

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

The archive mirrors the source **DL3DV-ALL-960P** layout: **one `.zip` per
scene**, grouped into bucket folders — `<bucket>/<scene_hash>.zip` (e.g.
`1K/001dccbc…740d5f.zip`). The scene hashes match DL3DV-ALL-960P, so the camera
annotations pair 1:1 with the source frames.

Each zip unpacks to the original per-frame layout:

```
dense/camera/frame_00001.json
dense/camera/frame_00002.json
...
```

Each JSON holds the predicted monocular camera parameters for that frame:

| 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 (`dense/camera/frame_00001.json`):

```json
{"roll": 0.0044, "pitch": 0.1566, "vfov": 1.0864, "k1": 0.0, "parse_ok": true}
```

## Camera Parameter Distributions

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

![dl3dv camera stats](analysis/dl3dv_camera_stats.png)

| split | roll μ / med / σ | pitch μ / med / σ | FoV μ / med / σ |
|-------|------------------|-------------------|-----------------|
| all (2.16M frames) | −0.3° / −0.3° / 4.9° | −7.0° / −6.1° / 16.0° | 53.4° / 54.8° / 8.7° |

**Reading the distributions**

- **Roll** is tightly peaked at 0° (σ ≈ 4.9°) — DL3DV captures are shot level.
- **Pitch** carries a downward bias (median ≈ −6°, mean ≈ −7°) with a wide
  spread (σ ≈ 16°), reflecting the varied up/down camera motion across scenes.
- **FoV** concentrates around **50–55°** (median ≈ 55°) — moderately wide video
  capture, ranging ~20–90°.

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/DL3DV-Absolute-Camera --repo-type dataset
```

### 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).


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

```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}
  }
```