EventVision is an AI-powered automated video editing system designed to seamlessly transform raw footage into polished, cinematic short-form videos (10-20 seconds). By leveraging advanced deep learning models to analyze the emotional context and themes of the footage, it intelligently applies professional visual filters, smooth transitions, and mood-matching background music. This creates production-ready content automatically, eliminating the need for manual video editing.
Utilizes the VideoMAE model (trained on Kinetics-400) to analyze video frames and detect underlying emotional themes.
Automatically selects and aligns background music that perfectly matches the detected emotion.
Applies fast OpenCV filters or optional ML-based Neural Style Transfer for a professional, cinematic look.
Programmatically applies smooth fade-in and fade-out effects for seamless scene progression.
Fully supports CUDA for rapid, GPU-accelerated video rendering and model inference.
Raw video files are loaded and parsed into frame sequences using MoviePy.
A sampled subset of frames is passed through the VideoMAE model to classify the dominant action and emotional context.
Based on the AI analysis, OpenCV or Neural Style Transfer algorithms apply specific color grading and cinematic filters.
The system retrieves an appropriate audio track, applies transition effects, and multiplexes the processed video with the new audio track.
Implement an audio analysis module to detect musical beats, ensuring video cuts happen exactly on the beat.
Expand the pipeline to ingest multiple raw clips and intelligently stitch the best moments into a cohesive narrative.
Wrap the core engine in a scalable backend framework to allow users to upload videos and render cinematic shorts via a web browser.
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