Edit-VAR: Taming Visual Autoregressive Model for Precise Video Editing

arXiv:2609.21268 · cs.CV · Submitted 2026-09-18 · Read on arXiv

cs.CV

Submitted: 2026-09-18

Updated: 2026-09-18

Code: https://github.com/RehgLab/RAVE

Project page: https://chongbozhao3-coder.github.io/EditVAR

Terminology

• Pruning — 31× in this paper · explained in 4 episodes
• Transformer — 15× in this paper · explained in 2 episodes
• Fidelity — 11× in this paper · explained in 2 episodes
• Alignment — 9× in this paper · explained in 1 episode
• Decoupling — 5× in this paper · explained in 2 episodes
• Redundancy — 4× in this paper · explained in 2 episodes
• Time Series Forecasting — 4× in this paper · explained in 3 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Aggregation — 3× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Decode — 3× in this paper · explained in 2 episodes
• Generative Model — 3× in this paper · explained in 2 episodes
• Generative Models — 3× in this paper · explained in 2 episodes
• KV cache — 3× in this paper · explained in 3 episodes
• Temporal consistency — 3× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Rectified Flow — 2× in this paper · explained in 3 episodes
• Active Regions — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Self-distillation — 1× in this paper · explained in 6 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Steering — 1× in this paper · explained in 2 episodes

Sources

• ToProVAR: Efficient Visual Autoregressive Modeling via Tri-Dimensional Entropy-Aware Semantic Analysis and Sparsity Optimization
• PAI-Studio: Cinematic Video Background Replacement with Camera-Aware Motion
• TokenFlow: Consistent Diffusion Features for Consistent Video Editing
• FastVAR: Linear Visual Autoregressive Modeling via Cached Token Pruning
• Prompt-to-Prompt Image Editing with Cross Attention Control
• EditVerse: Unifying Image and Video Editing and Generation with In-Context Learning
• FlowDirector: Training-Free Flow Steering for Precise Text-to-Video Editing
• FiVE: A Fine-grained Video Editing Benchmark for Evaluating Emerging Diffusion and Rectified Flow Models
• ControlVAR: Exploring Controllable Visual Autoregressive Modeling
• OPSD-V: On-Policy Self-Distillation for Post-Training Few-Step Autoregressive Video Generators
• Controllable Video Generation: A Survey
• Follow-Your-Creation: Empowering 4D Creation through Video Inpainting
• Follow-Your-Motion: Video Motion Transfer via Efficient Spatial-Temporal Decoupled Finetuning
• Follow-Your-Emoji-Faster: Towards Efficient, Fine-Controllable, and Expressive Freestyle Portrait Animation
• LiveLight: Real-time Streaming Video Relighting with Interactive Control
• FastVMT: Eliminating Redundancy in Video Motion Transfer
• EasyV2V: A High-quality Instruction-based Video Editing Framework
• SAM 2: Segment Anything in Images and Videos
• StreamingEffect: Real-Time Human-Centric Video Effect Generation
• VISTA: Triplet-Supervised Video Style Transfer with Diffusion Transformers

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