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.
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 GitHub Repository (Code): https://github.com/aharley/alltracker/ Hugging Face Model Page: https://huggingface.co/aharley/alltracker Gradio Demo: https://huggingface.co/spaces/aharley/alltracker
Dataset Usage and Preparation
This data is used by the training scripts in the associated GitHub repository. 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.
Citation
If you use this dataset or the associated code for your research, please cite the paper:
@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}
}