No Corners Cut: State-Grounded Transitions for Mid-Stream Prompt Switches in Video Generation

arXiv:2609.38691 · cs.CV · Submitted 2026-09-30 · Read on arXiv

cs.CV

Submitted: 2026-09-30

Updated: 2026-10-06

Terminology

• Benchmark — 36× in this paper · explained in 2 episodes
• Grounding — 16× in this paper · explained in 3 episodes
• Consistency — 13× in this paper · explained in 3 episodes
• Visual Grounding — 11× in this paper · explained in 2 episodes
• Distillation — 10× in this paper · explained in 2 episodes
• Fidelity — 8× in this paper · explained in 2 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Alignment — 7× in this paper · explained in 1 episode
• Diffusion Model — 6× in this paper · explained in 8 episodes
• Diffusion Models — 5× in this paper · explained in 6 episodes
• New-State Entry — 5× in this paper
• Old-State Exit — 5× in this paper
• World Model — 5× in this paper · explained in 7 episodes
• Conference on Computer Vision and Pattern Recognition — 4× in this paper
• Motion Boundary Smoothness — 4× in this paper
• Transition Pacing — 4× in this paper
• Distribution Matching — 3× in this paper · explained in 2 episodes
• Generative AI — 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
• Semantic Update Fidelity — 3× in this paper
• Trace — 3× in this paper · explained in 2 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Visual Boundary Smoothness — 3× in this paper
• Adaptation — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• RAG — 2× in this paper · explained in 3 episodes
• Segment Motion Continuity — 2× in this paper
• Segment Physical Plausibility — 2× in this paper
• Segment Prompt Fidelity — 2× in this paper
• Subject Preservation — 2× in this paper
• Ablation study — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Affordance — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• LoRA Adapters — 1× in this paper · explained in 3 episodes
• Task Planning — 1× in this paper · explained in 2 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
• Prompt Relay: Inference-Time Temporal Control for Multi-Event Video Generation
• LongLive-2.0: An NVFP4 Parallel Infrastructure for Long Video Generation
• Matrix-game 2.0: An open-source, real-time, and streaming interactive world model
• DirecT2V: Large Language Models are Frame-Level Directors for Zero-Shot Text-to-Video Generation
• LongLive-RAG: A General Retrieval-Augmented Framework for Long Video Generation
• Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion
• MemFlow: Flowing Adaptive Memory for Consistent and Efficient Long Video Narratives
• VideoDirectorGPT: Consistent Multi-scene Video Generation via LLM-Guided Planning
• Advancing Narrative Long Video Generation via Training-Free Identity-Aware Memory
• CausalCine: Real-Time Autoregressive Generation for Multi-Shot Video Narratives
• Qwen2.5 Technical Report
• SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning
• StreamHOI: Interaction-aware Temporal Memory Adaptation for Streaming HOI Video Generation
• SWIFT: Prompt-Adaptive Memory for Efficient Interactive Long Video Generation
• Advancing Open-source World Models
• Wan: Open and Advanced Large-Scale Video Generative Models
• TempAct: Advancing Temporal Plausibility in Autoregressive Video Generation via Planner-Executor RL

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