CamEdit50K: Focal Plane Subset

This dataset is part of the CamEdit project — a diffusion-based framework for continuous camera parameter control in photorealistic image editing. This subset focuses on focal plane manipulation, providing paired data for training models that can re-render images at different focal planes via text instructions.

Paper: CamEdit: Continuous Camera Parameter Control for Photorealistic Image Editing

Dataset Summary

Statistic	Value
Total image pairs	30,703
Unique input images	19,562
Unique edited images	29,790
Focal plane parameter range	[0.0, 1.0]
Image resolution	540–2,604 px (mean ~901 x 1,274)
Format	JPEG
Number of shards	11

Dataset Structure

Files

Each shard consists of a .tar archive containing the images and a .parquet file containing the metadata:

CamEdit_Focal_001.tar        # images (input/ and gt/ subfolders)
CamEdit_Focal_001.parquet    # metadata for this shard
...
CamEdit_Focal_011.tar
CamEdit_Focal_011.parquet
CamEditckpt_@_.tar           # pretrained LoRA checkpoints (AP / FP / SP)

Image Folder Layout

Each tar archive extracts to:

CamEdit_Focal_XXX/
├── input/          # original images
│   ├── 0000049.jpg
│   └── ...
└── gt/             # ground-truth edited images
    ├── 0000049-focal-0.3.jpg
    └── ...

Parquet Schema

Column	Type	Description
index	string	Unique sample ID
original_image_path	string	Relative path to the input image
edited_image_path	string	Relative path to the ground-truth edited image
instruction	string	Text instruction, e.g. "Render the image with focal plane 0.3."
height	int	Image height in pixels
width	int	Image width in pixels
task	string	Task type (Focal Plane); absent in shards 008–011
parameter	string	Focal plane value (0–1)

Per-Shard Statistics

Shard	Input Images	Pairs	Size
Focal_001	1,942	3,000	3.75 GB
Focal_002	1,941	3,000	3.70 GB
Focal_003	1,935	3,000	3.72 GB
Focal_004	1,961	3,000	3.79 GB
Focal_005	1,940	3,000	3.72 GB
Focal_006	1,936	3,000	3.66 GB
Focal_007	1,626	2,620	2.87 GB
Focal_008	530	3,000	1.17 GB
Focal_009	1,668	3,000	1.27 GB
Focal_010	3,000	3,000	2.16 GB
Focal_011	1,083	1,083	0.81 GB

Checkpoints

CamEditckpt_@_.tar contains pretrained LoRA weights and tokenizer embeddings for three camera parameter editing tasks:

• AP — Aperture
• FP — Focal Plane
• SP — Shutter Speed

Each subfolder includes:

• pytorch_lora_weights.safetensors
• Embed_one/, Embed_two/, Embed_three/ (learned camera token embeddings)
• tokenizer/, tokenizer_2/, tokenizer_3/

Usage

import pandas as pd, tarfile

# Read metadata
df = pd.read_parquet("CamEdit_Focal_001.parquet")

# Extract images
with tarfile.open("CamEdit_Focal_001.tar") as tar:
    tar.extractall(".")

# Access a sample
row = df.iloc[0]
print(row.instruction)        # "Render the image with focal plane 0.3."
print(row.original_image_path) # "CamEdit_Focal_001/input/0042680.jpg"
print(row.edited_image_path)   # "CamEdit_Focal_001/gt/0042680-focal-0.3.jpg"

License

This dataset is released under CC BY-NC 4.0.

Citation

If you use this dataset, please cite:

@article{qin2026camedit,
  title={Camedit: Continuous camera parameter control for photorealistic image editing},
  author={Qin, Xinran and Wang, Zhixin and Li, Fan and Chen, Haoyu and Pei, Renjing and Li, Wenbo and Cao, Xiaochun},
  journal={Advances in Neural Information Processing Systems},
  volume={38},
  pages={114152--114171},
  year={2026}
}