🌍 DeMark-World

A Universal Framework for AI Video Watermark Removal

DeMark-World is the evolution of SoraWatermarkCleaner. While its predecessor focused on Sora, DeMark-World is a "ready-to-roll" universal framework designed to detect and remove unwanted watermarks from any AI-generated video (Sora, Runway, Pika, Kling, etc.) with high fidelity and temporal consistency.

Prompt: a cat running in a forest.

Sora2	sora2_cat_run_sora_watermark_removed_combined_10M.mp4
Google Veo3.1	Veo3_Cat_Running_In_Forest_Video_sora_watermark_removed_combined_10M.mp4
Ruanway Gen4	gen4_cat_run_sora_watermark_removed_combined_10M.mp4

✨ Features

Moving beyond just Sora v2 in SoraWatermarkCleaner, DeMark-World is engineered to handle watermarks from the latest generation of video models, including Google Gemini/Veo, Runway Gen-3/Gen-4, Pika, Kling, and Luma Dream Machine.

🛠️ Installation

Prerequisites: You must have FFmpeg installed and added to your system PATH.

We use uv for project management. It is significantly faster and more reliable than pip/poetry.

1. Clone the repository

   Bash

   git clone https://github.com/linkedlist771/DeMark-World.git
   cd DeMark-World
   

2. Install environment

   # This creates the virtual environment and installs all dependencies
   uv sync
   

3. Activate environment

   # Linux/MacOS
   source .venv/bin/activate
   
   # Windows
   .venv\Scripts\activate
   

Note on Models: Detector weights and Inpainting models will be downloaded automatically to the cache directory upon the first run.

🚀 Quick Start

1. Interactive Web UI (Recommended)

The easiest way to use DeMark-World is via the modern Streamlit interface.

Bash

streamlit run app.py

Then open your browser to http://localhost:8501.

2. Python API

You can integrate DeMark-World into your own pipelines easily.

Python

from pathlib import Path
from src.demark_world.core import DeMarkWorld
from src.demark_world.schemas import CleanerType

if __name__ == "__main__":
    input_video = Path("resources/Veo3_Cat_Running_In_Forest_Video.mp4")
    output_video: Path = Path("outputs/cleaned.mp4")

    # Option 1: LaMa (Fast)
    demarker = DeMarkWorld(cleaner_type=CleanerType.LAMA)
    
    # Option 2: E2FGVI_HQ (High Quality + Time Consistent)
    # demarker = DeMarkWorld(cleaner_type=CleanerType.E2FGVI_HQ)

    demarker.run(input_video, output_video)

🧠 How It Works

DeMark-World operates in a two-stage pipeline, it just works like in SoraWatermarkCleaner.

📜 License

Distributed under the Apache 2.0 License. See LICENSE for more information.

🖊️ Citation

If you find this project helpful in your research or work, please cite:

@misc{DeMark-World2025,
  author = {linkedlist771},
  title = {DeMark-World},
  year = {2025},
  url = {https://github.com/linkedlist771/DeMark-World}
}

🙏 Acknowledgments

• For the incredible implementation of SOTA inpainting models.
• For the YOLO object detection framework.

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💝 If you find this project helpful, please consider starring the repo!