Video Diffusion Semantic Editing

# Video Diffusion Semantic Editing ## Description This project explores the hypothesis that incorporating user-guided semantic editing in the asynchronous video diffusion process can enhance control and personalization in generated video content. ## Research Hypothesis Incorporating user input to guide semantic aspects of video generation can lead to more personalized and diverse applications in fields like filmmaking and virtual reality. ## Implementation Approach The implementation involves developing a user interface for semantic labeling, integrating these inputs into a diffusion model, and evaluating the quality and satisfaction of generated videos. ## Setup Instructions 1. Clone the repository. 2. Install the required dependencies using `pip install -r requirements.txt`. 3. Prepare the video dataset as specified in the data_loader script. ## Usage Examples Run the training script: ```bash python src/train.py ``` Launch the user interface for semantic editing: ```bash python src/ui.py ``` ## Expected Results The model is expected to generate videos where semantic elements can be controlled and personalized through user input, enhancing the application scope of video synthesis models. ## References - [WorldWarp: Propagating 3D Geometry with Asynchronous Video Diffusion](http://arxiv.org/abs/2512.19678v1)