SCMAPR: Self-Correcting Multi-Agent Prompt Refinement for Complex-Scenario Text-to-Video Generation

2026-04-07

AI Summary

Existing text-to-video generation methods often suffer from degraded output quality in complex scenes due to ambiguous or insufficient prompts. This work proposes the first self-correcting multi-agent prompt optimization framework specifically designed for complex text-to-video generation. The framework enables staged, collaborative prompt refinement through scene-classification-driven agent routing, strategy-conditioned prompt rewriting, and structured semantic validation. To facilitate evaluation of complex-scene generation capabilities, the authors also introduce a new benchmark, T2V-Complexity. Experimental results demonstrate that the proposed method significantly outperforms current state-of-the-art approaches, achieving average performance gains of 2.67% on VBench, 3.28 on EvalCrafter, and 0.028 on T2V-CompBench.

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 T2V prompting. SCMAPR coordinates specialized agents to (i) route each prompt to a taxonomy-grounded scenario for strategy selection, (ii) synthesize scenario-aware rewriting policies and perform policy-conditioned refinement, and (iii) conduct structured semantic verification that triggers conditional revision when violations are detected. To clarify what constitutes complex scenarios in T2V prompting, provide representative examples, and enable rigorous evaluation under such challenging conditions, we further introduce {T2V-Complexity}, which is a complex-scenario T2V benchmark consisting exclusively of complex-scenario prompts. Extensive experiments on 3 existing benchmarks and our T2V-Complexity benchmark demonstrate that SCMAPR consistently improves text-video alignment and overall generation quality under complex scenarios, achieving up to 2.67\% and 3.28 gains in average score on VBench and EvalCrafter, and up to 0.028 improvement on T2V-CompBench over 3 State-Of-The-Art baselines.

Problem

• Text-to-Video generation
• complex scenarios
• prompt ambiguity
• underspecification
• prompt refinement

Innovation

• multi-agent prompt refinement
• self-correcting framework
• scenario-aware prompting
• text-to-video generation
• semantic verification