# EgoPlay: Event-Triggered Video Editing for Egocentric Streams

arXiv: https://arxiv.org/abs/2607.24560 · HTML: https://mlsanity.com/paper/2607.24560/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Video generation, one of 14 directions in Diffusion models.
- Builds on 29 of its 44 references that are in the map.
- First posted on arXiv 27 Jul 2026.

## Placed in

- Diffusion models / [Video generation](https://mlsanity.com/diffusion/video-generation/)

## Builds on (most cited first, 29 of 29)

- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [Qwen-Image Technical Report](https://mlsanity.com/paper/2508.02324/) (2025)
- [One-step Diffusion with Distribution Matching Distillation](https://mlsanity.com/paper/2311.18828/) (2023)
- [Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion](https://mlsanity.com/paper/2407.01392/) (2024)
- [VACE: All-in-One Video Creation and Editing](https://mlsanity.com/paper/2503.07598/) (2025)
- [Pyramidal Flow Matching for Efficient Video Generative Modeling](https://mlsanity.com/paper/2410.05954/) (2024)
- [UltraEdit: Instruction-based Fine-Grained Image Editing at Scale](https://mlsanity.com/paper/2407.05282/) (2024)
- [StreamingVLM: Real-Time Understanding for Infinite Video Streams](https://mlsanity.com/paper/2510.09608/) (2025)
- [Consistent Video-to-Video Transfer Using Synthetic Dataset](https://mlsanity.com/paper/2311.00213/) (2023)
- [Wan-Animate: Unified Character Animation and Replacement with Holistic Replication](https://mlsanity.com/paper/2509.14055/) (2025)
- [StreamForest: Efficient Online Video Understanding with Persistent Event Memory](https://mlsanity.com/paper/2509.24871/) (2025)
- [Streaming Video Understanding and Multi-round Interaction with Memory-enhanced Knowledge](https://mlsanity.com/paper/2501.13468/) (2025)
- [StreamMind: Unlocking Full Frame Rate Streaming Video Dialogue through Event-Gated Cognition](https://mlsanity.com/paper/2503.06220/) (2025)
- [InstructVid2Vid: Controllable Video Editing with Natural Language Instructions](https://mlsanity.com/paper/2305.12328/) (2023)
- [Video Editing via Factorized Diffusion Distillation](https://mlsanity.com/paper/2403.09334/) (2024)
- [HERMES: KV Cache as Hierarchical Memory for Efficient Streaming Video Understanding](https://mlsanity.com/paper/2601.14724/) (2026)
- [VEGGIE: Instructional Editing and Reasoning Video Concepts with Grounded Generation](https://mlsanity.com/paper/2503.14350/) (2025)
- [VidEdit: Zero-Shot and Spatially Aware Text-Driven Video Editing](https://mlsanity.com/paper/2306.08707/) (2023)
- [In-Context Learning with Unpaired Clips for Instruction-based Video Editing](https://mlsanity.com/paper/2510.14648/) (2025)
- [EffiVED:Efficient Video Editing via Text-instruction Diffusion Models](https://mlsanity.com/paper/2403.11568/) (2024)
- [VIVA: VLM-Guided Instruction-Based Video Editing with Reward Optimization](https://mlsanity.com/paper/2512.16906/) (2025)
- [Walk through Paintings: Egocentric World Models from Internet Priors](https://mlsanity.com/paper/2601.15284/) (2026)
- [EasyV2V: A High-quality Instruction-based Video Editing Framework](https://mlsanity.com/paper/2512.16920/) (2025)
- [Generated Reality: Human-centric World Simulation using Interactive Video Generation with Hand and Camera Control](https://mlsanity.com/paper/2602.18422/) (2026)
- [LOME: Learning Human-Object Manipulation with Action-Conditioned Egocentric World Model](https://mlsanity.com/paper/2603.27449/) (2026)
- [OmniInsert: Mask-Free Video Insertion of Any Reference via Diffusion Transformer Models](https://mlsanity.com/paper/2509.17627/) (2025)
- [EgoEdit: Dataset, Real-Time Streaming Model, and Benchmark for Egocentric Video Editing](https://mlsanity.com/paper/2512.06065/) (2025)
- [FunRec: Reconstructing Functional 3D Scenes from Egocentric Interaction Videos](https://mlsanity.com/paper/2604.05621/) (2026)
- [EgoReAct: Egocentric Video-Driven 3D Human Reaction Generation](https://mlsanity.com/paper/2512.22808/) (2025)

## Built on by (most cited first, 1 of 1)

- [ALIVE: Interaction-Aligned Object Insertion for First-Frame-Guided Video Editing](https://mlsanity.com/paper/2610.08779/) (2026)

## Abstract (arXiv)

We introduce EgoPlay, an event-triggered video-to-video editor for egocentric streams, obtained by fine-tuning a pretrained V2V diffusion transformer on event-conditioned data built primarily from Ego4D. Given a monocular video and an event-triggered prompt of the form "when X happens, do Y," EgoPlay infers whether and when event X occurs, preserves pre-event frames, and applies edit Y only to the post-event continuation. Rather than cascading a separate event detector with an editor, EgoPlay learns event recognition, temporal restraint, and pixel-level editing jointly in a single end-to-end model, while also handling negative and multi-event prompts. To support this, we construct a large-scale dataset of 106K event-triggered clip-prompt pairs spanning positive triggers, fabricated-trigger negatives, and multi-event prompts. We then train a bidirectional video diffusion editor with event-triggered supervision and derive a causal variant for chunk-by-chunk streamable inference. We further introduce an event-aware evaluation protocol that separately measures post-trigger editing quality, pre-trigger preservation, and false-trigger robustness. On the Ego4D benchmark, EgoPlay substantially outperforms EgoEdit, the state-of-the-art instruction-based egocentric video editing baseline, with relative gains of 17.7%, 16.9%, and 16.4% in editing quality, visual quality, and background consistency. It also surpasses a VLM-guided detector-editor baseline by 15.7%, 14.5%, and 13.5% on the same metrics, while using less than half the GPU memory.