Incorporating user-guided semantic editing during the asynchronous video diffusion process can enhance control and personalization in generated video content.

Motivation

WorldWarp's method primarily focuses on maintaining geometric consistency, but lacks user interactivity for fine-tuning specific semantic elements in the video. By allowing user input to guide semantic aspects, the model could cater to more personalized and diverse applications like filmmaking or virtual reality content creation.

Proposed Method

Integrate a user interface that allows for semantic label editing, which is then fed into the diffusion model as additional conditioning. Conduct experiments comparing the quality and user satisfaction of generated videos with and without user-guided inputs across various scenarios.

Expected Contribution

This research would expand the capabilities of video generation models to include user-driven customization, potentially broadening the application scope to personalized content creation.

Required Resources

Development of a user interface, access to labeled video datasets for training, and computational resources for retraining the diffusion model.

Source Paper

WorldWarp: Propagating 3D Geometry with Asynchronous Video Diffusion

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