README.md

---
pretty_name: InScene
language:
  - en
tags:
  - face
  - scene-restoration
  - image-restoration
  - diffusion
  - synthetic
  - facial-landmarks
  - identity
size_categories:
  - 10K<n<100K
task_categories:
  - image-to-image
  - text-to-image
---

# InScene

**InScene** is a synthetic dataset of full-body / in-the-wild scene images, each containing a person, paired with rich metadata (generation prompt, face bounding box, identity ID, and 68-point facial landmarks). It was created for the paper **Face2Scene: Using Facial Degradation as an Oracle for Diffusion-Based Scene Restoration** (CVPR 2026), where facial degradation is used as a supervisory signal ("oracle") for diffusion-based restoration of full scenes.

## Why this dataset

Faces are one of the most perceptually sensitive regions in an image and there exist strong, well-studied priors for facial quality. Face2Scene leverages this: by treating the face as an oracle for degradation, the model learns to restore the surrounding scene. To support this, InScene provides images in which a consistent set of **identities** appear across many diverse scenes, together with the localization metadata (face boxes and landmarks) needed to isolate and reason about the facial region. The `val` split additionally ships fixed **low-quality (degraded) inputs** paired with their clean targets, for consistent evaluation of restoration.

## Data generation

Images were produced with the **InfiniteYou-based data-generation pipeline** available here:

- 🔧 Pipeline: **https://github.com/amanwalia123/infiniteyoudatagen**

The **reference identity images** used to condition generation are taken from the **CelebRef-HQ** dataset, available from the DMDNet repository:

- 🖼️ Reference images (CelebRef-HQ): **https://github.com/csxmli2016/DMDNet**

Please refer to the pipeline repository for the full generation details (identity conditioning, prompt construction, sampling settings, etc.).

## Dataset structure

The dataset is split into `train/` and `val/`. Each split is a flat directory of **sample folders** (one folder per generated image). The folder name encodes provenance: `iden_<identity>_img_<source-face-id>_sample_<sample-id>_repeat_<k>` (the same prompt/identity may be sampled multiple times).

**`train`** — each folder holds the generated high-quality image and its metadata:

```
train/iden_00001_img_11_sample_321_repeat_1/
  iden_00001_img_11_sample_321_repeat_1.png     # 1024x1024 RGB image (HQ)
  iden_00001_img_11_sample_321_repeat_1.json    # metadata
```

**`val`** — each folder additionally holds a degraded, low-quality version of the image prefixed `LQ_`. The un-prefixed `*.png` is the **clean target** and the `LQ_*.png` is the **degraded restoration input**:

```
val/iden_00003_img_4_sample_473_repeat_1/
  iden_00003_img_4_sample_473_repeat_1.png      # 1024x1024 RGB image (HQ target)
  LQ_iden_00003_img_4_sample_473_repeat_1.png   # degraded input
  iden_00003_img_4_sample_473_repeat_1.json     # metadata
```

> Note: 6 of the val folders also contain a superseded copy of the trio prefixed `old_`
> (`old_*.png`, `old_LQ_*.png`, `old_*.json`), kept for provenance.

### Splits

| Split   | Sample folders | Identities | Files | Size  |
|---------|---------------:|-----------:|-------|------:|
| `train` |         11,260 |        905 | 11,260 HQ PNG + 11,260 JSON | 19 GB |
| `val`   |          1,329 |        100 | 1,329 HQ PNG + 1,329 LQ PNG + 1,329 JSON (+ 18 `old_*` leftover files) | 3.4 GB |

*(The `train` and `val` identity sets are disjoint. `train` contains no LQ images — degradation for training is expected to be applied on the fly.)*

### Per-sample JSON schema

The JSON describes the **HQ** image (`image_name` is the un-prefixed PNG); for `val`, the degraded counterpart is the same name prefixed with `LQ_`.

| Field        | Type            | Description                                                                 |
|--------------|-----------------|-----------------------------------------------------------------------------|
| `image_name` | string          | Filename of the paired HQ PNG.                                              |
| `prompt`     | string          | Structured scene prompt used for generation (subject, framing, background, detail/style). |
| `image_size` | object          | `{ "width": 1024, "height": 1024 }`.                                        |
| `face_bbox`  | list[int]       | Face bounding box `[x1, y1, x2, y2]` in pixel coordinates.                   |
| `identity`   | string          | Zero-padded identity ID; the same ID denotes the same person across samples.|
| `file_id`    | string          | Source reference face-image ID (from CelebRef-HQ) used to condition this identity. |
| `landmark`   | list[[int,int]] | 68 facial landmark points `[x, y]` in pixel coordinates.                    |

## Usage

The dataset is a folder-of-folders (not a parquet/`imagefolder` layout), so the simplest way to use it is to download and glob the sample folders:

```python
import glob, json, os
from PIL import Image
from huggingface_hub import snapshot_download

# Download one split (use allow_patterns="val/*" for val)
local = snapshot_download(
    "amir477/InScene",
    repo_type="dataset",
    allow_patterns="train/*",
)

samples = sorted(glob.glob(os.path.join(local, "train", "*")))
folder = samples[0]
name = os.path.basename(folder)

image = Image.open(os.path.join(folder, name + ".png"))      # 1024x1024 RGB (HQ)
meta = json.load(open(os.path.join(folder, name + ".json")))

print(image.size)            # (1024, 1024)
print(meta["identity"])      # e.g. "00001"
print(meta["face_bbox"])     # [x1, y1, x2, y2]
print(len(meta["landmark"])) # 68
```

For the `val` split, each sample also has a degraded input alongside the clean target:

```python
val_folder = sorted(glob.glob(os.path.join(local_val, "val", "*")))[0]
name = os.path.basename(val_folder)
target = Image.open(os.path.join(val_folder, name + ".png"))         # clean HQ target
degraded = Image.open(os.path.join(val_folder, "LQ_" + name + ".png"))  # degraded input
```

## Citation

If you find these images useful, please cite:

```bibtex
@inproceedings{kazerouni2026face2scene,
  title={Face2Scene: Using Facial Degradation as an Oracle for Diffusion-Based Scene Restoration},
  author={Kazerouni, Amirhossein and Suin, Maitreya and Aumentado-Armstrong, Tristan and Honari, Sina and Walia, Amanpreet and Mohomed, Iqbal and Derpanis, Konstantinos G and Taati, Babak and Levinshtein, Alex},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={8428--8438},
  year={2026}
}
```