Datasets:
| task_categories: | |
| - video-text-to-text | |
| language: | |
| - en | |
| license: cc-by-nc-4.0 | |
| tags: | |
| - instructional-videos | |
| - procedure-planning | |
| - diffusion-models | |
| # Masked Temporal Interpolation Diffusion (MTID) Dataset for Procedure Planning | |
| This repository contains the datasets used in the paper [Masked Temporal Interpolation Diffusion for Procedure Planning in Instructional Videos](https://huggingface.co/papers/2507.03393). | |
| The **MTID** (Masked Temporal Interpolation Diffusion) model addresses the challenge of procedure planning in instructional videos. It aims to generate coherent and task-aligned action sequences from start and end visual observations by leveraging a latent space temporal interpolation module to augment visual supervision with richer mid-state details. This dataset facilitates research and development in this area by providing necessary data for training and evaluating such models. | |
| The code for the MTID model is available at: [https://github.com/WiserZhou/MTID](https://github.com/WiserZhou/MTID) | |
| ## Data Preparation | |
| This dataset includes data for three widely used benchmark datasets: CrossTask, COIN, and NIV. | |
| To download datasets and features, navigate to the respective dataset directory and run the download script as shown in the original repository: | |
| ```bash | |
| cd ./dataset/{dataset_name} | |
| bash download.sh | |
| ``` | |
| Replace `{dataset_name}` with `crosstask`, `coin`, or `NIV`. | |
| Alternatively, you can find the datasets within this Hugging Face repository itself. | |
| ## Citation | |
| If you find this dataset or the associated paper useful in your research, please cite: | |
| ```bibtex | |
| @inproceedings{ | |
| zhou2025masked, | |
| title={Masked Temporal Interpolation Diffusion for Procedure Planning in Instructional Videos}, | |
| author={Yufan Zhou and Zhaobo Qi and Lingshuai Lin and Junqi Jing and Tingting Chai and Beichen Zhang and Shuhui Wang and Weigang Zhang}, | |
| booktitle={ICLR}, | |
| year={2025}, | |
| } | |
| ``` |