Memory-V2V: Memory-Augmented Video-to-Video Diffusion for Consistent Multi-Turn Editing

arXiv:2601.16296 · cs.CV, cs.AI, cs.LG · Submitted 2026-01-22 · Read on arXiv

cs.CV, cs.AI, cs.LG

Submitted: 2026-01-22

Updated: 2026-08-26

Code: https://github.com/hpcaitech/Open-Sora

Project page: https://dohunlee1.github.io/MemoryV2V/1

Terminology

• Consistency — 49× in this paper · explained in 3 episodes
• Diffusion Model — 19× in this paper · explained in 8 episodes
• Diffusion Models — 18× in this paper · explained in 6 episodes
• Tokenization — 11× in this paper · explained in 2 episodes
• Transformer — 11× in this paper · explained in 2 episodes
• Ablation — 8× in this paper · explained in 1 episode
• Rectified Flow — 6× in this paper · explained in 3 episodes
• Distillation — 4× in this paper · explained in 2 episodes
• Flow Matching — 4× in this paper · explained in 7 episodes
• Ablation study — 3× in this paper · explained in 1 episode
• Embeddings — 3× in this paper · explained in 2 episodes
• Fidelity — 3× in this paper · explained in 2 episodes
• Generative Model — 3× in this paper · explained in 2 episodes
• Latent Space — 3× in this paper · explained in 7 episodes
• Sparse Attention — 3× in this paper · explained in 5 episodes
• Distribution Matching — 2× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Optical Flow — 2× in this paper · explained in 2 episodes
• Temporal consistency — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Guided attention — 1× in this paper · explained in 2 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• ReCamMaster: Camera-Controlled Generative Rendering from A Single Video
• Scaling Instruction-Based Video Editing with a High-Quality Synthetic Dataset
• Mixture of Contexts for Long Video Generation
• LongAnimation: Long Animation Generation with Dynamic Global-Local Memory
• Goku: Flow Based Video Generative Foundation Models
• FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editing
• Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
• CCEdit: Creative and Controllable Video Editing via Diffusion Models
• TokenFlow: Consistent Diffusion Features for Consistent Video Editing
• Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion
• Reangle-A-Video: 4D Video Generation as Video-to-Video Translation
• Ground-A-Video: Zero-shot Grounded Video Editing using Text-to-image Diffusion Models
• LoViC: Efficient Long Video Generation with Context Compression
• VACE: All-in-One Video Creation and Editing
• LVSM: A Large View Synthesis Model with Minimal 3D Inductive Bias
• EditVerse: Unifying Image and Video Editing and Generation with In-Context Learning
• Auto-Encoding Variational Bayes
• VMem: Consistent Interactive Video Scene Generation with Surfel-Indexed View Memory
• Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
• OpenVid-1M: A Large-Scale High-Quality Dataset for Text-to-video Generation

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