| title | Video2GUI: Synthesizing Large-Scale Interaction Trajectories for Generalized GUI Agent Pretraining |
|---|---|
| authors | Weimin Xiong, Shuhao Gu, Bowen Ye, Zihao Yue, Lei Li, Feifan Song, Sujian Li, Hao Tian |
| date | 2026-05-14 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.14747 |
| affiliation | Peking University |
| upvotes | 46 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Recent advances in multimodal large language models have driven growing interest in graphical user interface (GUI) agents, yet their generalization remains constrained by the scarcity of large-scale training data spanning diverse real-world applications. Existing datasets rely heavily on costly manual annotations and are typically confined to narrow domains. To address this challenge, we propose Video2GUI, a fully automated framework that extracts grounded GUI interaction trajectories directly from unlabeled Internet videos. Video2GUI employs a coarse-to-fine filtering strategy to identify high-quality GUI tutorial videos and convert them into structured agent trajectories. Applying this pipeline to 500 million video metadata entries, we construct WildGUI, a large-scale dataset containing 12 million interaction trajectories spanning over 1,500 applications and websites. Pre-training Qwen2.5-VL and Mimo-VL on WildGUI yields consistent improvements of 5-20% across multiple GUI grounding and action benchmarks, matching or surpassing state-of-the-art performance. We will release both the WildGUI dataset and the Video2GUI pipeline to support future research of GUI agents. |
cite
@misc{xiong2026video,
title={Video2GUI: Synthesizing Large-Scale Interaction Trajectories for Generalized GUI Agent Pretraining},
author={Weimin Xiong and Shuhao Gu and Bowen Ye and Zihao Yue and Lei Li and Feifan Song and Sujian Li and Hao Tian},
year={2026},
eprint={2605.14747},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.14747}
}