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Memory-V2V: Augmenting Video-to-Video Diffusion Models with Memory research paper by Adobe, 2026

Adobe · Jan 22, 2026 · Multimodal and robotics · 1 citations · 28 upvotes · unverified

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What it shows

Memory-V2V enhances multi-turn video editing by maintaining cross-consistency through explicit memory mechanisms and efficient token compression in video-to-video diffusion models.

UnverifiedHugging Face's summary; not yet checked by hand.

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About this paper
Authors
Dohun Lee, Chun-Hao Paul Huang, Xuelin Chen and 3 more
arXiv
2601.16296 · PDF
Citations
1, 0 influential · Semantic Scholar
Upvotes
28 · Hugging Face
Code
github.com/DoHunLee1/Memory-V2V
Lab
Adobe · on Companies · on Acquisitions · on Quarterly · on Paydays · on TechConf

Changes

What changed
Influential citationsfirst count: 0Sep 25, 2026
Citationsfirst count: 1Sep 25, 2026
New paperFound by the weekly scan, unverifiedSep 25, 2026

Sources: each lab's own papers and arXiv, with citation and upvote counts from Semantic Scholar and Hugging Face. One-line summaries are for orientation, not a substitute for the paper. Logos via logo.dev; trademarks belong to their owners.

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