Agentic Physics-Grounded Video Generation via Memory-Augmented In-Context Reasoning

Key points

• Physical plausibility is essential for realistic video generation.1
• We therefore study a generate-diagnose-repair paradigm that preserves the original generation capability and selectively repairs physical inconsistencies afterward, raising two key questions: what to repair and how to repair?1
• Specifically, Spatiotemporal Causal Constraints identify the first invalid physical state and its causally affected states, producing a repair specification that guides physically plausible local regeneration while preserving unaffected content.1
• Experience-augmented In-context Learning combines the ongoing repair context with relevant experience from previous repairs, allowing the agent to adapt its repair decisions across diverse physical failures and accumulate completed trajectories for future cases.1

Sources (1)

• [1]Agentic Physics-Grounded Video Generation via Memory-Augmented In-Context Reasoning
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 04:08 PM

  To address them, we introduce PhysRepair, an agentic video generation framework that combines causal reasoning over the observed physical failure with prior repair experience to enable localized and adaptive correction.

  Physical plausibility is essential for realistic video generation.

Extractive summary: sentences quoted from the sources.