UniTime

This repository provides the official PyTorch implementation of "Universal Video Temporal Grounding with Generative Multi-modal Large Language Models" (NeurIPS 2025).

🌐 Project Page $\cdot$ 📄 Paper $\cdot$ 🤗 Model

🔥 News

• [2025.10] Released the code for data construction, training, and evaluation.
• [2025.09] UniTime accepted to NeurIPS 2025!
• [2025.06] Released the inference code.
• [2025.06] Preprint available on arXiv.

⚙️ Installation

conda create -n UniTime python=3.10
conda activate UniTime
pip install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu121
pip install -r requirements.txt

🚀 Quick Start

1. Download Model Checkpoints

   • Obtain the pretrained checkpoints from Qwen2-VL-7B and UniTime.
   • Set model_local_path to your local path for Qwen2-VL-7B, and model_finetune_path to your UniTime checkpoint.

2. Prepare Input Data

   • Create a JSON file for inference as data/test.json, and specify its path via the data_path argument.

3. Run Inference

   • Execute the following command to perform inference. The output results will be saved in the results/ directory.

   export CUDA_VISIBLE_DEVICES=0
   python inference.py --model_local_path path_to_qwen2vl7B \
        --model_finetune_path ckpt/unitime \
        --data_path data/test.json \
        --output_dir ./results/test \
        --nf_short 128

Data Preparation

1. Download the video and annotation files for each dataset from the corresponding source links.

2. Create the input file following the format below:

   [
       {
           "qid": 0, 
           "id": "3MSZA", 
           "annos": [
               {
                   "query": "person turn a light on.",
                   "window": [[24.3, 30.4]]
               }
           ],
           "duration": 30.96,
           "video_path": "./videos/3MSZA.mp4",
           "mode": "mr",
       }
   ]

   Example construction code for Ego4D-NLQ can be found in datasets/data_ego4d.py (see load_data_to_dict() function). Modify it as needed for other datasets.

3. (Optional) You may also download preprocessed annotations for each dataset from UniTime-Data.

Training and Evaluation

Execute the following commands in sequence:

# Feature Extraction
bash scripts/feature.sh

# Training
bash scripts/train.sh

# Evaluation
bash scripts/eval.sh

# Metrics
python eval_metrics.py --res ./results/RUN_NAME/results.json

Note: Modify the arguments marked with ToModify in the code according to the following definitions:

Argument	Description
path_to_qwen2vl7B	Path to the Qwen2-VL-7B model directory
path_to_feature_root	Root directory containing features for all datasets
path_to_video_root	Root directory path containing all video files
path_to_train_data	Path to training set annotation file generated by datasets/data_ego4d.py
path_to_val_data	Path to validation set annotation file generated by datasets/data_ego4d.py
path_to_test_data	Path to test set annotation file generated by datasets/data_ego4d.py
path_to_feature_folder	Subfolder under path_to_feature_root for a specific dataset
RUN_NAME	Experiment identifier/name for this training run

Citation

If you use this code and data for your research or project, please cite:

@inproceedings{unitime2025,
    title={Universal Video Temporal Grounding with Generative Multi-modal Large Language Models},
    author={Li, Zeqian and Di, Shangzhe and Zhai, Zhonghua and Huang, Weilin and Wang, Yanfeng and Xie, Weidi},
    booktitle={NeurIPS},
    year={2025}
}

Acknowledgements

This project builds upon several excellent open-source efforts:

• lmms-finetune
• LamRA.

Contact

For questions, please contact: [email protected].