Paper Detail
Shuai Yang, Bingjie Gao, Ziwei Liu, Jiaqi Wang, Dahua Lin, Tong Wu
Consistent video generation under editing operations requires persistence: when edits modify scene appearance or layout, subsequent generations should remain coherent across time and viewpoints. However, existing memory designs struggle to maintain long-term consistency after such modifications, as stored contexts may become outdated or invalid. To address this, we propose PermaVid, a novel framework built upon a multi-modal context memory that disentangles spatial context into semantic appearance and geometric structure, together with an edit-aware memory update and retrieval strategy that keeps memory evolution aligned with subsequent observations. Specifically, we develop two complementary memory banks: an RGB context memory that captures appearance-aware observations while implicitly encoding geometry, and a depth context memory that preserves geometry-only structure disentangled from semantics. Building on this design, we introduce a memory-guided video generation model that performs multi-modal feature fusion under reference conditions drawn from mixed-modality memory contexts. Experiments demonstrate that our method maintains strong long-term semantic and structural consistency after edits, significantly outperforming state-of-the-art methods.
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@misc{yang2026permavid,
title = {PermaVid: Consistent Video Generation Across Edits via Disentangled Context Memory},
author = {Shuai Yang and Bingjie Gao and Ziwei Liu and Jiaqi Wang and Dahua Lin and Tong Wu},
year = {2026},
abstract = {Consistent video generation under editing operations requires persistence: when edits modify scene appearance or layout, subsequent generations should remain coherent across time and viewpoints. However, existing memory designs struggle to maintain long-term consistency after such modifications, as stored contexts may become outdated or invalid. To address this, we propose PermaVid, a novel framework built upon a multi-modal context memory that disentangles spatial context into semantic appearan},
url = {https://huggingface.co/papers/2606.16449},
keywords = {multi-modal context memory, spatial context, semantic appearance, geometric structure, edit-aware memory update, memory retrieval strategy, RGB context memory, depth context memory, multi-modal feature fusion, memory-guided video generation, code available, huggingface daily},
eprint = {2606.16449},
archiveprefix = {arXiv},
}
{}