SCMAPR: Self-Correcting Multi-Agent Prompt Refinement for Complex-Scenario Text-to-Video Generation
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 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 condi
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 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.
Executive Summary
The article introduces SCMAPR, a novel multi-agent framework designed to address the persistent challenges in text-to-video (T2V) generation under complex scenarios. By decomposing prompt refinement into a structured, stage-wise process—including scenario routing, policy-conditioned rewriting, and semantic verification—SCMAPR enhances text-video alignment and generation quality. The authors further contribute a benchmark, T2V-Complexity, to standardize evaluation of complex prompts. Empirical results demonstrate significant improvements over state-of-the-art baselines, with gains of up to 2.67%, 3.28%, and 0.028 on established benchmarks. This work bridges a critical gap in T2V systems by formalizing complexity and proposing scalable, interpretable refinement mechanisms.
Key Points
- ▸ SCMAPR addresses ambiguity and underspecification in text prompts for T2V generation by employing a multi-agent system that iteratively refines prompts through structured, scenario-aware policies.
- ▸ The framework introduces a taxonomy-grounded scenario routing mechanism to select appropriate refinement strategies based on prompt complexity, followed by policy-conditioned rewriting and semantic verification to ensure compliance.
- ▸ A new benchmark, T2V-Complexity, is introduced to systematically evaluate complex scenarios in T2V generation, enabling rigorous assessment of refinement frameworks like SCMAPR.
Merits
Innovative Multi-Agent Architecture
The staged, agent-based approach to prompt refinement is conceptually robust, leveraging specialization to handle diverse and ambiguous prompts, while maintaining interpretability through verifiable semantic checks.
Empirical Rigor and Benchmark Contribution
The introduction of T2V-Complexity provides a much-needed standardized framework for evaluating complex scenarios, and the reported gains over multiple baselines underscore the method's effectiveness.
Scalability and Generalizability
The modular design of SCMAPR allows for extension to other generative modalities or domains, suggesting broader applicability beyond text-to-video generation.
Demerits
Computational Overhead
The multi-agent framework may introduce additional latency and resource consumption, particularly for real-time applications, due to the iterative refinement and verification steps.
Taxonomy Dependency
The reliance on a predefined taxonomy for scenario routing could limit adaptability to novel or unforeseen complex scenarios not captured in the taxonomy, potentially constraining generalization.
Evaluation Scope
While T2V-Complexity is a valuable contribution, its comprehensiveness in representing the full spectrum of real-world complex scenarios remains an open question, particularly given the rapidly evolving nature of user inputs.
Expert Commentary
SCMAPR represents a significant advancement in addressing the longstanding challenge of prompt ambiguity in text-to-video generation. By formalizing complex scenarios and introducing a multi-agent, self-correcting refinement process, the authors have demonstrated a scalable and interpretable solution that outperforms existing baselines. The modular design is particularly noteworthy, as it allows for incremental improvements and adaptability to new scenarios. However, the framework's reliance on a predefined taxonomy and the potential computational overhead are non-trivial challenges that warrant further exploration. From a policy perspective, the introduction of T2V-Complexity underscores the importance of standardized benchmarks in generative AI, a trend that aligns with increasing calls for transparency and accountability in AI systems. This work not only advances the technical frontier but also sets a precedent for rigorous, scenario-aware evaluation in generative AI.
Recommendations
- ✓ Future research should explore methods to reduce the computational overhead of multi-agent refinement, such as adaptive routing or early termination of refinement cycles for simpler prompts.
- ✓ Expanding the taxonomy to include a wider range of complex scenarios, including those derived from user studies or real-world applications, would enhance the framework's robustness and generalizability.
- ✓ Collaborative efforts to develop cross-modal benchmarks for complex scenarios could unify evaluation standards across generative AI systems, fostering interoperability and comparability.
- ✓ Investigation into the integration of SCMAPR with reinforcement learning-based reward models could further improve refinement policies by incorporating user feedback or preference data.
Sources
Original: arXiv - cs.AI