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LongV-EVAL: A Benchmark for Long Video Editing Evaluation

LongV-EVAL is a benchmark dataset designed for evaluating text-driven long video editing methods. It consists of 75 high-quality videos, each approximately one minute long, covering diverse domains such as landscapes, people, and animals. The dataset provides meticulously annotated editing prompts for three aspects: foreground, background, and style, enabling comprehensive evaluation of editing quality, temporal consistency, and semantic alignment.

Dataset Structure

The dataset is organized into four folders:

  • videos/: Contains 75 MP4 files of source videos (original unedited videos).
  • foreground/: Includes 75 text files with prompts focusing on foreground object editing (e.g., changing object attributes or replacing objects).
  • background/: Includes 75 text files with prompts for background modification (e.g., altering scene context or tone).
  • style/: Includes 75 text files with prompts for artistic style transfer (e.g., applying styles like Van Gogh, watercolor, or Picasso).