EgoPlay: Event-Triggered Video Editing for Egocentric Streams

TL;DR - EgoPlay is an end-to-end diffusion model that detects prompted events in egocentric video and edits only the post-event footage. It improves editing quality over detector-editor pipelines while using less than half their GPU memory.

• Fine-tunes a pretrained video-to-video diffusion transformer on 106K event-conditioned clip-prompt pairs derived primarily from Ego4D.
• Jointly learns event recognition, pre-trigger preservation, and pixel-level editing, including negative and multi-event prompts.
• Provides bidirectional editing and a causal variant for chunk-by-chunk streaming inference.
• Outperforms EgoEdit by 17.7% in editing quality, 16.9% in visual quality, and 16.4% in background consistency.