| # SteerVTE: Precise Video Text Editing with Style and Glyph Control |
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| <div align="left"> |
| <p align="left"> |
| <a href="https://arxiv.org/abs/2606.23254"><img src="https://img.shields.io/badge/Paper-arXiv-b31b1b.svg?logo=arxiv" alt="Paper"></a> |
| <a href="https://zengkaiya.github.io/SteerVTE/"><img src="https://img.shields.io/badge/Homepage-Website-blue?logo=github" alt="Homepage"></a> |
| <a href="https://huggingface.co/MewtwoX23/SteerVTE/tree/main"><img src="https://img.shields.io/badge/Weights-HuggingFace-yellow?logo=huggingface" alt="Weights"></a> |
| <a href="https://opensource.org/license/apache-2.0"><img src="https://img.shields.io/badge/LICENSE-Apache-green.svg" alt="License"></a> |
| </p> |
| </div> |
| |
| ## 💡 Abstract |
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| Visual text editing aims to precisely modify text in images and videos while preserving stylistic consistency and visual realism. |
| Despite significant advances in the image domain, video text editing remains largely unexplored: it is a localized task demanding stroke-level precision within small text regions, which compounds the challenges of cross-frame accuracy, temporal coherence, and stylistic fidelity. |
| We introduce 🔥SteerVTE🔥, a unified framework that <b>steer</b>s a frozen video diffusion model to perform precise <b>V</b>ideo <b>T</b>ext <b>E</b>diting through style and glyph control. |
| Built on a frozen diffusion transformer, SteerVTE attaches a lightweight text context adapter with two complementary modules: a style encoder capturing the original text's visual attributes, and dual-granularity glyph encoders encoding the target text at both the line and character levels. |
| To overcome the inherently weak text rendering priors of video foundation models, we further propose a glyph-aware spatial-focal loss and a three-stage progressive training curriculum that scales from image to video data. |
| To support large-scale training, we also develop an automatic synthesis pipeline and construct SteerVTE-1M, a dataset of one million triplets spanning diverse scenes, fonts, and stylistic effects. |
| Extensive experiments demonstrate that SteerVTE substantially outperforms existing video editing baselines across text accuracy, style consistency, and temporal coherence. |
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| ## 📊 Dataset |
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| VTE-Bench is a benchmark for video text editing, covering synthetic and real-world |
| scenes with diverse text content, visual styles, and motion patterns. Each sample |
| contains the source video, the target video or editing condition, a text mask, and |
| reference images used to evaluate text editing quality. |
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| The dataset is organized into three subsets: |
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| - `Synth`: synthetic video text editing samples; |
| - `Real`: real-world video text editing samples; |
| - `SceneText`: scene text editing samples collected from diverse videos. |
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| The corresponding CSV files (`Synth.csv`, `Real.csv`, and `SceneText.csv`) provide |
| the sample metadata and relative paths to the videos, masks, and reference images. |
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| ## 📚 Citation |
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| If you find SteerVTE is useful in your research or applications, please consider giving us a star 🌟 and citing it by the following BibTeX entry. |
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| ```bibtex |
| @article{zeng2026steervte, |
| title={SteerVTE: Seamless Video Text Editing with Style and Glyph Control}, |
| author={Zeng, Kai and Li, Moran and Wang, Zhengwei and Yu, Yingchen and Lin, Yiheng and An, Ruichuan and Lu, Ming and She, Qi and Zhang, Wentao}, |
| journal={arXiv preprint arXiv:2606.23254}, |
| year={2026} |
| } |
| ``` |
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