Datasets:
pretty_name: T2exture Dataset
task_categories:
- image-to-image
tags:
- thermal-video
- lwir
- frame-interpolation
- texture-propagation
gated: true
extra_gated_heading: Request access
extra_gated_description: >-
Please request access before downloading this dataset. If you use the dataset,
you must cite the T2exture paper.
extra_gated_button_content: Request access
T2exture Dataset
Prepared synthetic and real thermal sequences for the two-stage T2exture method.
Access
This dataset uses Hugging Face gated access. Please request access before downloading. No additional institution or intended-use form is required.
Citation requirement
If you use this dataset in a paper, thesis, report, benchmark, software release, model card, or public result, you must cite:
Jiashuo Chen, Cheng Dai, Yanan Hu, and Fanglin Bao. T2exture: Sparsely Perturbed Thermal-to-Texture Imaging. arXiv:2608.02192, 2026. DOI: 10.48550/arXiv.2608.02192.
BibTeX:
@article{chen2026t2exture,
title = {T$^2$exture: Sparsely Perturbed Thermal-to-Texture Imaging},
author = {Chen, Jiashuo and Dai, Cheng and Hu, Yanan and Bao, Fanglin},
journal = {arXiv preprint arXiv:2608.02192},
year = {2026},
doi = {10.48550/arXiv.2608.02192},
url = {https://arxiv.org/abs/2608.02192}
}
Please also report the dataset URL and release commit used in your experiment:
https://huggingface.co/datasets/chenjiashuo/T2exture_datasets
The machine-readable citation is provided in CITATION.cff.
Download
After access is approved:
python -c "from huggingface_hub import snapshot_download; snapshot_download(repo_id='chenjiashuo/T2exture_datasets', repo_type='dataset', local_dir='datasets')"
Layout
datasets/
train.txt
valid.txt
test.txt
dataset_manifest.json
sim/<scene>/
texture/001.npy # prepared target residual X
passive/001.npy # passive/source-off state S^off
source_on/001.npy # optional raw S^on for exact anchor construction
flow/s10/<scene>/001_002_011.npz
source_off/amt-s/<scene>/001.npy
source_off/amt-l/<scene>/001.npy
source_off/amt-g/<scene>/001.npy
real/<sequence>/...
Synthetic frames are single-channel NumPy arrays. Real benchmark frames are grayscale PNG images. The prepared synthetic files store X directly in texture/; source_on/ is optional and is copied when present by the dataset preparation script.
Protocol
The default protocol uses an active-frame stride of 10, a centered passive context of 5, and flow/s10:
C_t = [S^off_{t-2}, S^off_{t-1}, S^off_t, S^off_{t+1}, S^off_{t+2}]
Formal T2exture-S/L/G runs use the matching Stage 1 cache:
T2exture-S -> source_off/amt-s
T2exture-L -> source_off/amt-l
T2exture-G -> source_off/amt-g
These caches contain estimated S_hat^off frames at active keyframes. Stage 2 uses them both to correct active texture anchors and to fill the passive context at active keyframes.
Local Check
After downloading this dataset as datasets/, run from the code repository root:
python -B scripts/preflight.py --data-root datasets --config train.yaml --require-source-off
The check verifies split files, frame shape, flow files, and all three Stage 1 cache folders.
Excluded Files
Training logs, evaluation outputs, generated visual sheets, previews, and local caches are not dataset artifacts and should stay outside this repository.