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arXiv Computer Vision By Shuzhou Yang, Lanqing Guo, Lingen Li, Weiqi Li, Zhao Zhong, Liefeng Bo, Jian Zhang

UniVOE: A Removal-Driven Framework for Interaction-Aware Universal Video Object Editing

Read the original on arXiv Computer Vision →

UniVOE is a removal‑driven framework that trains both video object removal and injection models. It first builds an effect‑aware remover by leveraging video inpainting priors and fine‑tuning on object‑present/object‑absent pairs, then uses the remover to generate large‑scale paired data for training an injection model that restores objects with their dynamics and interaction effects. A reward‑based post‑training step aligns injection results with human preferences, and experiments show UniVOE outperforms existing open‑source and commercial systems, especially in challenging interaction‑effect scenarios.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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