arXiv Computer Vision
InfinityEdit: Infinite Video Editing with a Lightweight Edit-Ignition Adapter
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arXiv Computer Vision
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StreamErase: Towards Ultra-Fast Streaming Video Erasing
arXiv:2610.20137v1 Announce Type: new Abstract: Current video object removal methods suffer from prohibitive latency (minutes per video) and non-causal processing that prevents real-time interaction....
arXiv AI
Jul 28
EgoPlay: Event-Triggered Video Editing for Egocentric Streams
arXiv:2607. 24560v1 Announce Type: cross Abstract: 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.
Hugging Face Trending Papers
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EgoPlay: Event-Triggered Video Editing for Egocentric Streams
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.
Hugging Face Trending Papers
2d ago
StreamErase: Towards Ultra-Fast Streaming Video Erasing
Current video object removal methods suffer from prohibitive latency (minutes per video) and non-causal processing that prevents real-time interaction. We present StreamErase, enabling sub-second late...
arXiv Computer Vision
Oct 9
Transforming Image Editors into Video Editors
The paper introduces Anchor-based Video Editing (AVE), a two-stage framework that converts a powerful image editor into a video editor by first editing keyframes and then propagating those edits across time using a motion-guided image-to-video diffusion model. AVE treats edited keyframes as fixed anchors, leveraging the strengths of modern image editors while avoiding costly end-to-end video training. Experiments on IVEBench and VIE-Bench demonstrate strong performance in instruction following, temporal consistency, and content fidelity, and ablations show that video quality correlates with the underlying image editor’s performance.
arXiv Machine Learning
Aug 27
Memory-V2V: Memory-Augmented Video-to-Video Diffusion for Consistent Multi-Turn Editing
Memory-V2V is a memory‑augmented video‑to‑video diffusion framework designed to improve cross‑turn consistency in multi‑turn video editing. It stores previous outputs in an external memory, retrieves relevant edits, and incorporates them via relevance‑aware tokenization and adaptive compression, allowing scalable conditioning without linear computational growth. Experiments on iterative video novel view synthesis and text‑guided long video editing show that Memory‑V2V enhances consistency while preserving visual quality and outperforming strong baselines with modest overhead.