SCMAPR: Self-Correcting Multi-Agent Prompt Refinement for Complex-Scenario Text-to-Video Generation
📰 ArXiv cs.AI
SCMAPR is a self-correcting multi-agent prompt refinement framework for complex-scenario text-to-video generation
Action Steps
- Formulate complex-scenario prompt refinement as a stage-wise multi-agent refinement process
- Implement SCMAPR framework to refine text prompts
- Utilize diffusion models for text-to-video generation
- Evaluate and refine the framework for improved performance
Who Needs to Know This
AI engineers and researchers working on text-to-video generation tasks can benefit from SCMAPR, as it improves the accuracy and specificity of text prompts in complex scenarios
Key Insight
💡 SCMAPR framework can improve the accuracy and specificity of text prompts in complex scenarios for text-to-video generation
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🚀 SCMAPR: Self-Correcting Multi-Agent Prompt Refinement for complex-scenario text-to-video generation
Key Takeaways
SCMAPR is a self-correcting multi-agent prompt refinement framework for complex-scenario text-to-video generation
Full Article
Title: SCMAPR: Self-Correcting Multi-Agent Prompt Refinement for Complex-Scenario Text-to-Video Generation
Abstract:
arXiv:2604.05489v1 Announce Type: new Abstract: Text-to-Video (T2V) generation has benefited from recent advances in diffusion models, yet current systems still struggle under complex scenarios, which are generally exacerbated by the ambiguity and underspecification of text prompts. In this work, we formulate complex-scenario prompt refinement as a stage-wise multi-agent refinement process and propose SCMAPR, i.e., a scenario-aware and Self-Correcting Multi-Agent Prompt Refinement framework for
Abstract:
arXiv:2604.05489v1 Announce Type: new Abstract: Text-to-Video (T2V) generation has benefited from recent advances in diffusion models, yet current systems still struggle under complex scenarios, which are generally exacerbated by the ambiguity and underspecification of text prompts. In this work, we formulate complex-scenario prompt refinement as a stage-wise multi-agent refinement process and propose SCMAPR, i.e., a scenario-aware and Self-Correcting Multi-Agent Prompt Refinement framework for
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