| license: mit | |
| task_categories: | |
| - image-to-image | |
| library_name: | |
| - datasets | |
| tags: | |
| - point-tracking | |
| - optical-flow | |
| - video | |
| - dense-correspondence | |
| # AllTracker: Efficient Dense Point Tracking Dataset | |
| This repository contains the data produced/postprocessed as part of [**AllTracker: Efficient Dense Point Tracking at High Resolution**](https://huggingface.co/papers/2506.07310). | |
| AllTracker is a model that estimates long-range point tracks by estimating the flow field between a query frame and every other frame of a video. This dataset supports the training and evaluation of such models, providing high-resolution and dense correspondence fields. | |
| **Project Page:** [https://alltracker.github.io](https://alltracker.github.io) | |
| **GitHub Repository (Code):** [https://github.com/aharley/alltracker/](https://github.com/aharley/alltracker/) | |
| **Hugging Face Model Page:** [https://huggingface.co/aharley/alltracker](https://huggingface.co/aharley/alltracker) | |
| **Gradio Demo:** [https://huggingface.co/spaces/aharley/alltracker](https://huggingface.co/spaces/aharley/alltracker) | |
| ## Dataset Usage and Preparation | |
| This data is used by the training scripts in the associated [GitHub repository](https://github.com/aharley/alltracker/). For detailed instructions on how to download, prepare, and use this dataset for training, please refer to the [**"Data prep" section in the GitHub repository's README**](https://github.com/aharley/alltracker/#data-prep). | |
| ## Citation | |
| If you use this dataset or the associated code for your research, please cite the paper: | |
| ```bibtex | |
| @inproceedings{harley2025alltracker, | |
| author = {Adam W. Harley and Yang You and Xinglong Sun and Yang Zheng and Nikhil Raghuraman and Yunqi Gu and Sheldon Liang and Wen-Hsuan Chu and Achal Dave and Pavel Tokmakov and Suya You and Rares Ambrus and Katerina Fragkiadaki and Leonidas J. Guibas}, | |
| title = {All{T}racker: {E}fficient Dense Point Tracking at High Resolution}, | |
| booktitle = {ICCV}, | |
| year = {2025} | |
| } | |
| ``` |