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

arXiv: https://arxiv.org/abs/2604.05489 · HTML: https://mlsanity.com/paper/2604.05489/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Video generation, one of 14 directions in Diffusion models.
- Builds on 27 of its 148 references that are in the map.
- First posted on arXiv 7 Apr 2026.

## Placed in

- Diffusion models / [Video generation](https://mlsanity.com/diffusion/video-generation/)

## Builds on (most cited first, 27 of 27)

- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://mlsanity.com/paper/2408.06072/) (2024)
- [VBench: Comprehensive Benchmark Suite for Video Generative Models](https://mlsanity.com/paper/2311.17982/) (2023)
- [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022)
- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [EvalCrafter: Benchmarking and Evaluating Large Video Generation Models](https://mlsanity.com/paper/2310.11440/) (2023)
- [T2V-CompBench: A Comprehensive Benchmark for Compositional Text-to-video Generation](https://mlsanity.com/paper/2407.14505/) (2024)
- [Identity-Preserving Text-to-Video Generation by Frequency Decomposition](https://mlsanity.com/paper/2411.17440/) (2024)
- [MIDI: Multi-Instance Diffusion for Single Image to 3D Scene Generation](https://mlsanity.com/paper/2412.03558/) (2024)
- [PhyT2V: LLM-Guided Iterative Self-Refinement for Physics-Grounded Text-to-Video Generation](https://mlsanity.com/paper/2412.00596/) (2024)
- [Investigating Prompt Engineering in Diffusion Models](https://mlsanity.com/paper/2211.15462/) (2022)
- [LinGen: Towards High-Resolution Minute-Length Text-to-Video Generation with Linear Computational Complexity](https://mlsanity.com/paper/2412.09856/) (2024)
- [GROOT: Generating Robust Watermark for Diffusion-Model-Based Audio Synthesis](https://mlsanity.com/paper/2407.10471/) (2024)
- [The Devil is in the Prompts: Retrieval-Augmented Prompt Optimization for Text-to-Video Generation](https://mlsanity.com/paper/2504.11739/) (2025)
- [InstanceCap: Improving Text-to-Video Generation via Instance-aware Structured Caption](https://mlsanity.com/paper/2412.09283/) (2024)
- [DiffProsody: Diffusion-based Latent Prosody Generation for Expressive Speech Synthesis with Prosody Conditional Adversarial Training](https://mlsanity.com/paper/2307.16549/) (2023)
- [BlobGEN-Vid: Compositional Text-to-Video Generation with Blob Video Representations](https://mlsanity.com/paper/2501.07647/) (2025)
- [Kiss3DGen: Repurposing Image Diffusion Models for 3D Asset Generation](https://mlsanity.com/paper/2503.01370/) (2025)
- [Scene Splatter: Momentum 3D Scene Generation from Single Image with Video Diffusion Model](https://mlsanity.com/paper/2504.02764/) (2025)
- [Nexus: An Omni-Perceptive And -Interactive Model for Language, Audio, And Vision](https://mlsanity.com/paper/2503.01879/) (2025)
- [Prompt Refinement with Image Pivot for Text-to-Image Generation](https://mlsanity.com/paper/2407.00247/) (2024)
- [CTRL-D: Controllable Dynamic 3D Scene Editing with Personalized 2D Diffusion](https://mlsanity.com/paper/2412.01792/) (2024)
- [ByTheWay: Boost Your Text-to-Video Generation Model to Higher Quality in a Training-free Way](https://mlsanity.com/paper/2410.06241/) (2024)
- [Re-Attentional Controllable Video Diffusion Editing](https://mlsanity.com/paper/2412.11710/) (2024)
- [CARFT: Boosting LLM Reasoning via Contrastive Learning with Annotated Chain-of-Thought-based Reinforced Fine-Tuning](https://mlsanity.com/paper/2508.15868/) (2025)
- [SGDPO: Self-Guided Direct Preference Optimization for Language Model Alignment](https://mlsanity.com/paper/2505.12435/) (2025)
- [EIDT-V: Exploiting Intersections in Diffusion Trajectories for Model-Agnostic, Zero-Shot, Training-Free Text-to-Video Generation](https://mlsanity.com/paper/2504.06861/) (2025)

## Built on by (most cited first, 4 of 4)

- [When Cultures Move: Measuring and Improving Multicultural Text-to-Video Generation](https://mlsanity.com/paper/2605.16716/) (2026)
- [Agentic Visual Generation: From Generative Models to Agentic Control](https://mlsanity.com/paper/2609.06758/) (2026)
- [WanPE: Towards Cinematic Prompt Enhancement for Modern Text-to-Video Generation](https://mlsanity.com/paper/2609.30221/) (2026)
- [Towards Unified Evaluation of Prompt Enhancers for Video Generation](https://mlsanity.com/paper/2610.11736/) (2026)

## Abstract (arXiv)

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. The codes of SCMAPR are publicly available at https://github.com/HiThink-Research/SCMAPR.