Jinfang Gan¹, Wenzheng Zeng^1,2*, Yang Xiao^1†, Xintao Zhang¹, Chaoyang Zheng¹, Ran Zhao¹,
Ran Wang^3,4, Min Du⁵, Zhiguo Cao¹

¹Huazhong University of Science and Technology, ²National University of Singapore,
³School of Journalism and Information Communication, HUST, ⁴School of Future Technology, HUST, ⁵ByteDance

DeFB achieves a superior accuracy-efficiency balance compared to other SOTA methods.

This repository contains the official implementation of the AAAI 2026 paper "DeFB: Decomposed Feature Learning for Real-Time Multi-Person Eyeblink Detection in Untrimmed In-the-Wild Videos".

• 🔍 Rethinking Unified Models: We identify two critical limitations in existing unified multi-person eyeblink detection models: (1) feature granularity conflict between face localization and eyeblink detection, and (2) unstable face-eye feature learning during joint training.

• 🧩 Decomposed Feature Learning: We propose DeFB, which models faces and eyes in granularity-specific feature spaces. This enables fine-grained spatio-temporal modeling for eyeblink detection while maintaining efficiency for face localization.

• ⚡ Asynchronous Training Strategy: We adopt an asynchronous learning mechanism where eye feature learning refines well-trained coarse face features, significantly improving training stability and convergence.

• 🏆 State-of-the-Art Performance: DeFB doubles the performance compared to previous SOTA (Blink-AP: 24.65% vs. 10.11%) while boosting efficiency by nearly 35%.

• 🔌 Plug-and-Play Capability: DeFB can be integrated as a plug-in to substantially augment the eyeblink detection capabilities of general action detectors.

1. Create a new conda environment:

   conda create -n defb python=3.9
   conda activate defb

2. Install PyTorch (2.0.1+ is recommended):

   pip install torch>=2.0.1 torchvision>=0.15.2

3. Install other dependencies:

   pip install -r requirements.txt

1. Download the MPEblink dataset from Zenodo.

2. Organize the dataset as follows:

   data/
   └── mpeblink/
       ├── videos/
       │   ├── train/
       │   └── val/
       ├── annotations/
       │   ├── train.json
       │   └── val.json
       └── raw_frames/        # Generated in next step
   

3. Convert videos to raw frames:

   python tools/mpeblink_build_raw_frames_dataset.py --root $YOUR_DATA_PATH

4. Update the dataset path in configs/dataset/mpeblink.yml.

We provide a video introduction of our work:

We provide a complete pipeline script run_mpeblinkv1.sh that includes all stages:

bash run_mpeblinkv1.sh

The pipeline consists of the following stages:

# First phase training (blink_len=10)
torchrun --master_port=9909 --nproc_per_node=2 tools/train.py \
    --use-amp --seed=0 \
    -c configs/rtdetrv2/detrs-blink_len=10_mpeblinkv1.yml \
    -t rtdetrv2_r50vd_6x_coco_ema.pth

# Second phase training (blink_len=30), loading stage 1 weights
torchrun --master_port=9909 --nproc_per_node=2 tools/train.py \
    --use-amp --seed=0 \
    -c configs/rtdetrv2/detrs-blink_len=30_mpeblinkv1.yml \
    -t output/rtdetrv2_r50vd_6x_coco_len=10_mpeblinkv1/last.pth

# Inference on validation set
python infer_trainset.py \
    --config configs/rtdetrv2/detrs-blink_len=30_mpeblinkv1.yml \
    --output mpeblink_v1 \
    --checkpoint output/rtdetrv2_r50vd_6x_coco_len=30_mpeblinkv1/checkpoint0000.pth \
    --json $YOUR_DATA_PATH/annotations/val.json \
    --root $YOUR_DATA_PATH/val_rawframes \
    --mode val

# Inference on training set for blink module
python infer_trainset.py \
    --config configs/rtdetrv2/detrs-blink_len=30_mpeblinkv1.yml \
    --output mpeblink_v1 \
    --checkpoint output/rtdetrv2_r50vd_6x_coco_len=10_mpeblinkv1/checkpoint0000.pth \
    --json $YOUR_DATA_PATH/annotations/train.json \
    --root $YOUR_DATA_PATH/train_rawframes \
    --mode train

# Split dataset for blink detection
python BlinkModel/split_dataset.py --config configs/BlinkModule/full_v1.py

# Train blink detection module
python BlinkModel/train_blink_detector.py --config configs/BlinkModule/full_v1.py

# Full model testing
python test.py \
    --track_config configs/rtdetrv2/detrs-blink_len=30_mpeblinkv1.yml \
    --blink_config configs/BlinkModule/full_v1.py \
    --output mpeblink_v1 \
    --checkpoint output/rtdetrv2_r50vd_6x_coco_len=30_mpeblinkv1/checkpoint0000.pth \
    --json $YOUR_DATA_PATH/annotations/test.json \
    --root $YOUR_DATA_PATH/test_rawframes \
    --mode test

# Convert results with threshold
python tools/instblink_plus_result_convertor_args.py \
    --json results/test_results/mpeblink_v1.json \
    --output results/blink_converted_results/mpeblink_v1.json \
    --threshold 0.07

# Evaluate on MPEblink
python tools/eval_mpeblink.py \
    --gt_json $YOUR_DATA_PATH/annotations/test.json \
    --pred_json results/blink_converted_results/mpeblink_v1.json

This code is built upon RT-DETRv2 and InstBlink. We thank the authors for their excellent work.

If you find our work useful in your research, please consider citing:

@misc{gan2026defb,
    title={DeFB: Decomposed Feature Learning for Real-Time Multi-Person Eyeblink Detection in Untrimmed In-the-Wild Videos},
    author={Gan, Jinfang and Zeng, Wenzheng and Xiao, Yang and Zhang, Xintao and Zheng, Chaoyang and Zhao, Ran and Wang, Ran
  and Du, Min and Cao, Zhiguo},
    howpublished={\url{https://github.com/jinfanggan/DeFB}},
    note={Accepted at AAAI 2026},
    year={2026}
  }

If you use the MPEblink dataset, please also cite:

@inproceedings{zeng2023real,
  title={Real-time Multi-person Eyeblink Detection in the Wild for Untrimmed Video},
  author={Zeng, Wenzheng and Xiao, Yang and Wei, Sicheng and Gan, Jinfang and Zhang, Xintao and Cao, Zhiguo and Fang, Zhiwen and Zhou, Joey Tianyi},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages={13854--13863},
  year={2023}
}

This project is released under the Apache 2.0 license.

For questions and suggestions, please open an issue or contact Jinfang Gan (jinfanggan@hust.edu.cn).