Memory-V2V: Augmenting Video-to-Video Diffusion Models with Memory
Recent foundational video-to-video diffusion models have achieved impressive results in editing user provided videos by modifying appearance, motion, or camera movement. However, real-world video editing is often an iterative process, where users refine results across multiple rounds of interaction. In this multi-turn setting, current video editors struggle to maintain cross-consistency across sequential edits. In this work, we tackle, for the first time, the problem of cross-consistency in multi-turn video editing and introduce Memory-V2V, a simple, yet effective framework that augments existing video-to-video models with explicit memory. Given an external cache of previously edited videos, Memory-V2V employs accurate retrieval and dynamic tokenization strategies to condition the current editing step on prior results. To further mitigate redundancy and computational overhead, we propose a learnable token compressor within the DiT backbone that compresses redundant conditioning tokens while preserving essential visual cues, achieving an overall speedup of 30%. We validate Memory-V2V on challenging tasks including video novel view synthesis and text-conditioned long video editing. Extensive experiments show that Memory-V2V produces videos that are significantly more cross-consistent with minimal computational overhead, while maintaining or even improving task-specific performance over state-of-the-art baselines. Project page: https://dohunlee1.github.io/MemoryV2V
6976d29a5d41524304c1350a
Dohun Lee
6976d29a5d41524304c1350b
Chun-Hao Paul Huang
6976d29a5d41524304c1350c
Xuelin Chen
6976d29a5d41524304c1350d
Jong Chul Ye
6976d29a5d41524304c1350e
Duygu Ceylan
6976d29a5d41524304c1350f
Hyeonho Jeong
claimed_verified
2026-01-27T09:43:54.037Z
656ee8008bb9f4f8d95bd8f7
/avatars/4069d70f1279d928da521211c495d638.svg
Hyeonho Jeong
hyeonho-jeong-video
user
hyeonho-jeong-video
Memory-V2V: Augmenting Video-to-Video Diffusion Models with Memory
Recent foundational video-to-video diffusion models have achieved impressive results in editing user provided videos by modifying appearance, motion, or camera movement. However, real-world video editing is often an iterative process, where users refine results across multiple rounds of interaction. In this multi-turn setting, current video editors struggle to maintain cross-consistency across sequential edits. In this work, we tackle, for the first time, the problem of cross-consistency in multi-turn video editing and introduce Memory-V2V, a simple, yet effective framework that augments existing video-to-video models with explicit memory. Given an external cache of previously edited videos, Memory-V2V employs accurate retrieval and dynamic tokenization strategies to condition the current editing step on prior results. To further mitigate redundancy and computational overhead, we propose a learnable token compressor within the DiT backbone that compresses redundant conditioning tokens while preserving essential visual cues, achieving an overall speedup of 30%. We validate Memory-V2V on challenging tasks including video novel view synthesis and text-conditioned long video editing. Extensive experiments show that Memory-V2V produces videos that are significantly more cross-consistent with minimal computational overhead, while maintaining or even improving task-specific performance over state-of-the-art baselines. Project page: https://dohunlee1.github.io/MemoryV2V
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.
video-to-video diffusion models cross-consistency multi-turn video editing memory augmentation retrieval dynamic tokenization token compressor DiT backbone video novel view synthesis text-conditioned long video editing