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Scaling Zero-Shot Reference-to-Video Generation research paper by Meta, 2025

Meta · Dec 7, 2025 · Multimodal and robotics · 10 citations · 29 upvotes · unverified

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What it shows

Saber is a scalable zero-shot framework for reference-to-video generation that uses video-text pairs to learn identity-consistent representations and outperforms models trained with explicit reference data.

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

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About this paper
Authors
Zijian Zhou, Shikun Liu, Haozhe Liu and 14 more
arXiv
2512.06905 · PDF
Venue
arXiv.org
Citations
10, 0 influential · Semantic Scholar
Upvotes
29 · Hugging Face
Code
github.com/franciszzj/Saber
Lab
Meta · on Companies · on Acquisitions · on Quarterly · on Paydays · on TechConf

Changes

What changed
Influential citationsfirst count: 0Sep 25, 2026
Citationsfirst count: 10Sep 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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