jianfeng0369/VidLLVIP

Temporally and Spatially Aligned Infrared-Visible Video Dataset

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VidLLVIP is an unofficial processed paired infrared-visible video dataset derived from the raw LLVIP videos. The dataset provides temporally aligned, spatially registered, quality-checked, 5-second video pairs for video fusion, cross-modal registration, and multimodal video understanding.

VidLLVIP is derived from LLVIP. Please follow the original LLVIP license and citation requirements when using or redistributing this dataset.

📰 News

• 🚀 2026-05-06: We released the VidLLVIP dataset.
• 🎉 2026-02-05: Our multimodal video fusion paper CMVF was accepted by Information Fusion. The code is available in the CMVF GitHub repository.

Download

The large video files are distributed separately:

• Option 1: Download from Hugging Face
• Option 2: Download from Quark Drive

After downloading, if you want to reproduce the full pipeline, extract datamaker.zip and matrix.zip and place them under the corresponding datamaker/ directory; extract raw.zip and place it under the corresponding raw/ directory. If you only want to use the final dataset directly, extract dataset.zip and place it under the corresponding dataset/ directory.

Highlights

• Built from 14 source infrared-visible video pairs, numbered 01 to 14.
• Provides 894 final 5-second paired clips with one IR video and one VI video per sample.
• Uses same-name files under dataset/ir and dataset/vi as the pairing rule.
• Final clip format: 1280 x 1024, 25 FPS, 125 frames, no audio.
• Includes scripts for temporal alignment, spatial registration, checkerboard quality inspection, and 5-second clip generation.

Dataset Snapshot

Item	Value
Source	LLVIP raw infrared-visible videos
Processed source pairs	14 pairs, IDs 01-14
Final paired clips	894 pairs
Modalities	Infrared (ir) and visible (vi)
Clip length	5 seconds
Resolution	1280 x 1024
Frame rate	25 FPS
Frames per clip	125
Pairing rule	Same file name under dataset/ir and dataset/vi

Repository Layout

VidLLVIP/
  README.md
  README_zh-CN.md
  raw/
    videos/{ir,vi}/        # Original LLVIP videos before alignment
  datamaker/
    01_time_align.py       # Temporal alignment
    02_space_align.py      # Spatial registration
    03_checkerboard.py     # Checkerboard QA videos
    04_split_5s_videos.py  # 5-second clip generation
    requirements.txt
    matrix/                # 3x3 perspective matrices for IDs 01-14
    01_align/              # Time-aligned full videos and timestamp sheets
    02_warp/               # Spatially registered full videos
    03_ckboard/            # Checkerboard QA videos
  dataset/
    ir/                    # Final infrared clips
    vi/                    # Final visible clips
  figs/                    # README figures

Data Format

Final clips are stored as paired files:

dataset/
  ir/01_0000_0005.mp4
  vi/01_0000_0005.mp4

The file name format is:

{source_id}_{start_second}_{end_second}.mp4

For example, 01_0000_0005.mp4 means source video 01, from 0s to 5s. The same file name in dataset/ir and dataset/vi forms one paired sample.

Quick Start

If you only need the final paired clips, read dataset/ir and dataset/vi directly:

from pathlib import Path

root = Path("dataset")

for ir_path in sorted((root / "ir").glob("*.mp4")):
    vi_path = root / "vi" / ir_path.name
    assert vi_path.exists(), f"Missing visible pair: {vi_path}"
    # Load ir_path and vi_path with your video reader.

To reproduce the preprocessing pipeline, install the Python dependencies:

cd datamaker
conda create -n vidllvip python=3.10 -y
conda activate vidllvip
pip install -r requirements.txt

The system also needs ffmpeg and ffprobe on PATH.

Reproduce the Dataset

1. Temporal Alignment

python 01_time_align.py

Inputs:

• raw/videos/ir/{id}.mp4
• raw/videos/vi/{id}.mp4

Outputs:

• datamaker/01_align/{id}/ir.mp4
• datamaker/01_align/{id}/vi.mp4
• datamaker/01_align/{id}/timestamp.xlsx

The script reads frame timestamps, chooses the shorter stream as the base, and matches the other modality with monotone nearest-frame matching. The default maximum timestamp gap is 0.08s.

2. Spatial Registration

python 02_space_align.py

Inputs:

• datamaker/01_align/{id}/ir.mp4
• datamaker/01_align/{id}/vi.mp4
• datamaker/matrix/{id}.csv

Outputs:

• datamaker/02_warp/{id}/ir.mp4
• datamaker/02_warp/{id}/vi.mp4

The script warps IR frames into the VI coordinate system with a 3x3 perspective matrix, then crops both modalities to 1280 x 1024.

3. Checkerboard QA

python 03_checkerboard.py

Inputs:

• datamaker/02_warp/{id}/ir.mp4
• datamaker/02_warp/{id}/vi.mp4

Outputs:

• datamaker/03_ckboard/{id}.mp4

The checkerboard videos alternate IR and VI blocks, making edge continuity and object alignment easier to inspect by eye.

4. Split Into 5-Second Clips

python 04_split_5s_videos.py

Inputs:

• datamaker/02_warp/{id}/ir.mp4
• datamaker/02_warp/{id}/vi.mp4

Outputs:

• dataset/ir/{id}_{start}_{end}.mp4
• dataset/vi/{id}_{start}_{end}.mp4

The default window and stride are both 5s. Tails shorter than 5s are skipped.

Suggested Uses

• Video fusion: use same-name clips from dataset/ir and dataset/vi.
• Cross-modal registration: use datamaker/01_align as temporally aligned but spatially unregistered input, and datamaker/02_warp as the registered reference.
• Joint fusion and registration: train registration on datamaker/01_align, then train or evaluate fusion on datamaker/02_warp or dataset/.

Figures

The figs/ directory is ordered by first appearance in this README:

File	Purpose
figs/01_overview.jpg	Dataset overview and key statistics.
figs/02_sample_pairs.jpg	Representative IR/VI/fusion frame examples.
figs/03_pipeline.jpg	End-to-end construction pipeline.
figs/04_dataset_structure.jpg	Released file structure and pairing rule.
figs/05_alignment_quality.jpg	Temporal and spatial alignment quality checks.

Citation

VidLLVIP is an unofficial processed version derived from the raw LLVIP infrared and visible videos. If you use VidLLVIP or the processing scripts, registration matrices, or paired video clips in this repository, please also follow the original LLVIP license and citation requirements.

1. Original Dataset Citation

VidLLVIP is derived from LLVIP. When using this dataset, please first cite the original LLVIP dataset:

@inproceedings{jia2021llvip,
  title     = {LLVIP: A visible-infrared paired dataset for low-light vision},
  author    = {Jia, Xinyu and Zhu, Chuang and Li, Minzhen and Tang, Wenqi and Zhou, Wenli},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages     = {3496--3504},
  year      = {2021}
}

2. VidLLVIP Citation

If you use the processed VidLLVIP dataset, registration matrices, or preprocessing pipeline provided by this project, please also cite VidLLVIP:

@dataset{ding2026vidllvip,
  author  = {Ding, Jianfeng},
  title   = {VidLLVIP: A visible-infrared paired video dataset for low-light vision},
  year    = {2026},
  version = {v1.0.0},
  url     = {https://github.com/jianfeng0369/VidLLVIP}
}

3. Related Paper Citation

CMVF is an infrared and visible video fusion method based on spatio-temporal consistency and designed for unregistered inputs. If your research uses the CMVF method or code, or is related to infrared and visible video fusion, please also consider citing the following paper:

@article{cmvf2026ding,
  title   = {CMVF: Cross-modal unregistered video fusion via spatio-temporal consistency},
  journal = {Information Fusion},
  volume  = {132},
  pages   = {104212},
  year    = {2026},
  issn    = {1566-2535},
  author  = {Jianfeng Ding and Hao Zhang and Zhongyuan Wang and Jinsheng Xiao and Xin Tian and Zhen Han and Jiayi Ma}
}

Contact

If you have any questions, please contact: jianfeng0369@gmail.com.