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cloud.google.com·6h ago
AI Research
arXiv CS.AI·11d ago·5 min read
ViTeX-Bench: Benchmarking High-Fidelity Video Scene Text Editing
TL;DR
ViTeX-Bench introduces a benchmark for high-fidelity video scene text editing, featuring a dataset of 387 videos and a three-axis evaluation protocol. It highlights the challenges of achieving accurate text replacement while maintaining visual quality and temporal consistency.
✦ Why It Matters
Explore ViTeX-Bench to enhance your video editing tools and improve text editing accuracy in your projects.
Key Takeaways
- Utilize ViTeX-Bench for benchmarking video text editing tools.
- Leverage the ViTeX-Dataset for training your models.
- Focus on achieving high CharAcc for better text accuracy.
- Consider temporal consistency as a key metric in video editing.
Full Summary
Video scene text editing aims to replace text in videos, such as signs or labels, while preserving the original scene's dynamics. ViTeX-Bench addresses the gap in high-quality video editing by providing a benchmark suite that includes the ViTeX-Dataset, which contains 387 real-world 720p videos with text-region masks and editing instructions.
The evaluation protocol assesses text correctness, visual quality, and temporal consistency through 13 metrics. Among the evaluated baselines, achieving accurate text replacement alongside temporal stability and scene preservation proved challenging.
The open-source ViTeX-Edit-14B editor, fine-tuned on the dataset, achieved a CharAcc score of 0.688, the highest among video-native editors. ViTeX-Bench serves as a reproducible foundation for further research in video scene text editing, emphasizing the need for improved methodologies in this area.