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Streaming Video Editing with Easy Adaptation

Paper recorded by Signals 4 on 2026-09-21 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-21 on arXiv · recorded by Signals 4 on 2026-09-22

Category: cs.CV · 计算机视觉 · first seen 2026-09-22

Abstract

In this paper, we propose SVEET, a framework that requires merely training on a pretrained bidirectional video diffusion model but supports high-quality streaming video editing in an auto-regressive fashion. To tackle this problem, we first systematically revisit existing video-to-video diffusion approaches and identify two key principles for such streaming adaptation: backbone feature disentangle

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#14 most recent of 270 cs.CV papers we have recorded · ↑ newer: Toward a foundation model for forest point clouds · ↓ older: Virtual neural networks: hundreds of souls in a body
Cite this page: Streaming Video Editing with Easy Adaptation: the #14 most recent of 270 cs.CV papers we have recorded (as of 2026-09-21). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/streaming-video-editing-with-easy-adaptation.html
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