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.
Figures & tables
Figure 1: Self-Correcting Multi-Agent Prompt Refinement Framework (SCMAPR). SCMAPR organizes prompt refinement as a stage-wise multi-agent collaboration involving six specialized agents. The framework proceeds through five functional stages: (I) Scenario Routing , where Scenario Router assigns a scenario tag to the input prompt. (II) Policy Synthesis , where a Policy Generator generates a scenario-conditioned rewriting policy. (III) Policy-Conditioned Refinement , where a Prompt Refiner rewrites the prompt. (IV) Semantic Verification , where Atomizer and Validator collaboratively verify semantic fidelity through atomic extraction and entailment judgment. (V) Conditional Revision , where verification feedback conditionally triggers targeted revision, enabling self-correcting refinement.
Figure 2: Illustration of the Semantic Verification Stage in SCMAPR. Given a user input and the corresponding refined prompt, semantic verification is performed in four steps. (1) Atomic Extraction decomposes the user input into atom elements. (2) Chunking segments the refined prompt into semantically coherent evidence units. (3) Atom-Chunk Matching retrieves the most relevant evidence chunk for each atom. (4) Entailment Validation assesses atom-level semantic relations between atoms and evidence chunks. Through this design, semantic missing and contradictions in the refined prompt can be detected and subsequently used to trigger downstream revision.
Method
Average Score
Aesthetic Quality
Background Consistency
Imaging Quality
Motion Smoothness
Subject Consistency
Temporal Flickering
LaVie
81.89
53.52
95.01
62.83
95.15
90.96
93.85
LaVie + Open-Sora
81.95
53.67
94.82
62.93
95.28
91.24
93.78
LaVie + RAPO
82.80
54.38
95.76
63.06
96.03
93.21
94.35
LaVie + SCMAPR
84.56
56.53
97.43
63.87
97.18
95.95
96.39
Wan
86.19
63.75
95.41
69.78
97.62
95.12
95.43
Wan + Open-Sora
86.27
63.82
95.22
70.05
97.76
95.28
95.52
Table 1: Quantitative results (%) on VBench with LaVie and Wan as T2V backbones. Bold values denote the best performance for each metric under each backbone.
Method
Average
Motion Quality
Text-Video Alignment
Visual Quality
Temporal Consistency
LaVie
62.12
53.19
69.60
64.81
60.87
LaVie + Open-Sora
62.78
53.07
71.38
65.26
61.41
LaVie + RAPO
63.91
53.34
74.38
66.62
61.29
LaVie + SCMAPR
65.18
53.87
75.42
68.84
62.56
Wan
63.46
53.79
73.05
65.17
61.85
Wan + Open-Sora
63.95
53.93
73.84
65.60
62.43
Table 2: Quantitative comparisons on EvalCrafter. SCMAPR consistently achieves better results, demonstrating a clear lead on this benchmark.
Method
Average
Consistent Attribute
Dynamic Attribute
Action Binding
Motion Binding
LaVie
0.388
0.620
0.232
0.483
0.215
LaVie + Open-Sora
0.361
0.532
0.214
0.470
0.226
LaVie + RAPO
0.460
0.692
0.267
0.635
0.243
LaVie + SCMAPR
0.476
0.704
0.273
0.640
0.285
Wan
0.454
0.694
0.263
0.591
0.269
Wan + Open-Sora
0.446
0.672
0.258
0.583
0.274
Table 3: Quantitative comparisons on T2V-CompBench. SCMAPR achieves the highest average score.
Method
Average Score
Aesthetic Quality
Background Consistency
Imaging Quality
Motion Smoothness
Subject Consistency
Temporal Flickering
Wan
82.95
56.95
94.83
63.80
96.27
91.28
94.59
Wan + SCMAPR
85.69
59.32
96.24
67.87
98.87
93.94
97.92
Table 4: Quantitative results (%) on T2V-Complexity using VBench metrics with Wan as the T2V backbone.
T2V Benchmark
Prompts
CS Prompts
CS Proportion
CS Categories
VBench
946
311
32.9%
9
EvalCrafter
700
422
60.3%
9
T2V-CompBench
1400
972
69.4%
10
T2V-Complexity
1000
1000
100.0%
10
Table 5: Statistics of complex-scenario prompts across different T2V benchmarks. Here, CS is the abbreviation for complex-scenario.
Method
Average Score
SCMAPR
88.21%
w/o Scenario Routing
86.49%
w/o Policy Generation
87.75%
w/o Verification & Self-Correction
87.63%
Table 6: Ablation studies of different components in SCMAPR on VBench.
Figure 3: Comparisons of videos generated using Wan ( Wang et al., 2025a ) conditioned on user input and refined prompts from SCMAPR.
Appendix figures & tables16 assets
Supplementary material from the paper’s appendix.
Appendix
Taxonomy Category
Core Difficulty
Non-overlap Rationale
Abstract Descriptions
Mapping non-visual, metaphorical, or symbolic language to coherent visual realizations.
Not tied to concrete visual structures; focuses on metaphorical or symbolic interpretation.
Complex Spatial Relations
Satisfying explicit geometric constraints (e.g., left, right, between or center) with stable layout, depth perception and occlusion reasoning.
Independent of entity count, interaction, or physics; concerns layout, depth, and occlusion.
Multi-Element Scenes
Preserving all salient elements, entity counts, and scene completeness under high visual density without omissions or unintended merging.
Requires stable configurations among multiple entities; does not involve contact or force dynamics.
Fine-Grained Appearance
Sustaining high-frequency details such as textures, text, and identity-specific features across frames.
Addresses textures, identity, and small-scale details; orthogonal to spatial, temporal, and physical reasoning.
Temporal Consistency
Avoiding temporal drift, flickering, or motion discontinuities in cross-frame evolution.
Focuses on continuity of motion and appearance across frames; unrelated to layout or object count.
Stylistic Hybrids
Enforcing coherent visual style under mixed or evolving artistic domains without degradation.
Fully decoupled from spatial, physical, or interaction constraints; concerns aesthetic specification.
Appendix
Table A.1: Summary of the ten complex-scenario taxonomy categories, including each category’s core difficulty for text-to-video generation and the rationale used to ensure non-overlapping primary-label annotation.
Operation
Duration (s)
1 GPU (%)
2 GPUs (%)
4 GPUs (%)
8 GPUs (%)
Scenario Routing
2.2
0.44%
0.85%
1.42%
2.11%
Policy Generation
5.9
1.19%
2.28%
3.82%
5.65%
Prompt Refinement
2.8
0.57%
1.08%
1.81%
2.68%
Entailment Verification
14.7
2.97%
5.68%
9.51%
14.07%
Content Revision
1.9
0.38%
0.74%
1.23%
1.82%
Video Generation
—
94.44%
89.36%
82.20%
73.68%
Appendix
Table A.2: Time cost distribution of each operation under different numbers of GPUs for T2V generation.
Operation
Duration (s)
Video Generation (1 GPU)
467
Video Generation (2 GPUs)
231
Video Generation (4 GPUs)
127
Video Generation (8 GPUs)
77
Appendix
Table A.3: Time cost of video generation under different numbers of GPUs.
Figure A.1: Examples of hallucination elimination after prompt refinement by SCMAPR.
Figure A.2: Examples of hallucination elimination after prompt refinement by SCMAPR. The hallucinated information is highlighted in red, and its elimination or correction is marked in green.
Figure A.3: Distribution of complex scenarios across VBench.
Figure A.4: Distribution of complex scenarios across EvalCrafter.
Figure A.5: Distribution of complex scenarios across CompBench.
Figure A.6: Distribution of prompt lengths on VBench and EvalCrafter (in words).
Figure A.7: Distribution of prompt lengths on T2V-CompBench and T2V-Complexity (in words).
VBench
Quantity
Proportion
rewrite once
818
86.47%
rewrite twice
118
12.47%
rewrite 3 times
10
1.06%
Total
946
100.00%
Appendix
Table A.4: Statistics of rewrite rounds on VBench.
T2V-Complexity
Quantity
Proportion
rewrite once
823
82.30%
rewrite twice
151
15.10%
rewrite 3 times
24
2.40%
rewrite 4 times
2
0.20%
Total
1000
100.00%
Appendix
Table A.5: Statistics of rewrite rounds on T2V-Complexity.
Figure A.8: An end-to-end case study of SCMAPR with self-correction. Given a user input, the framework performs scenario routing, policy generation, policy-conditioned prompt refinement, atom-level verification, and targeted revision. Entailment Validator labels each atom-evidence pair and conditionally triggers targeted revision, producing a verified refined prompt for downstream video generation.
Figure A.9: Qualitative comparison of generated videos under an abstract user input (user-provided prompt) and refined prompt. Sampled frames generated from the user-provided prompt (top) and the prompt refined by SCMAPR (bottom) are contrasted. The original prompt yields a visually plausible but semantically drifting dark-water scene, while the refined prompt concretizes the intended metaphor and produces a consistent depiction of a flower bud emerging through cracked dark soil with a warm glow, better matching the target concept.
Figure A.10: Qualitative comparison of generated videos under a user input (user-provided prompt) and refined prompt from T2V-Complexity benchmark.
Figure A.11: Qualitative comparison of generated videos under a user input (user-provided prompt) and refined prompt from T2V-Complexity benchmark.
Text-to-video (T2V) generation has rapidly progressed in visual fidelity, yet its ability to faithfully represent multiple cultures within a single prompt remains underexplored. We introduce MAVEN, a multi-agent prompt refinement framework designed to improve cultural fidelity in both mono-cultural and cross-cultural T2V generation. MAVEN decomposes prompts into person, action, and location dimensions, handled by specialized agents operating in parallel or sequentially. To support systematic evaluation, we contribute a new benchmark of 243 culturally grounded prompts and 972 corresponding videos, spanning three cultures (Chinese, American, Romanian), three action categories, and both mono-cultural and cross-cultural scenarios. Evaluations combining CLIP-based metrics, VLM-as-judge assessments, and videoquality measures show that multi-agent refinement, particularly parallel specialization, significantly improves cultural relevance while preserving visual quality and temporal consistency. The dataset and code are available at https://github.com/AIM-SCU/MAVEN
While large-scale datasets have driven significant progress in Text-to-Video (T2V) generative models, these models remain highly sensitive to input prompts, demonstrating that prompt design is critical to generation quality. Current methods for improving video output often fall short: they either depend on complex, post-editing models, risking the introduction of artifacts, or require expensive fine-tuning of the core generator, which severely limits both scalability and accessibility. In this work, we introduce 3R, a novel RAG based prompt optimization framework. 3R utilizes the power of current state-of-the-art T2V diffusion model and vision language model. It can be used with any T2V model without any kind of model training. The framework leverages three key strategies: RAG-based modifiers extraction for enriched contextual grounding, diffusion-based Preference Optimization for aligning outputs with human preferences, and temporal frame interpolation for producing temporally consistent visual contents. Together, these components enable more accurate, efficient, and contextually aligned text-to-video generation. Experimental results demonstrate the efficacy of 3R in enhancing the static fidelity and dynamic coherence of generated videos, underscoring the importance of optimizing user prompts.
Text-to-video (T2V) models are highly sensitive to prompt specificity, while iterative prompt optimization typically improves generation quality only through repeated video generation and vision-language model evaluation at inference time. We propose TIDE, a framework that distills this costly search process into an interpretable and reusable prompt-rewriting Skill. TIDE formulates prompt rewriting as a set of source-conditioned rule modules, each targeting a distinct semantic dimension. Once distilled, the frozen Skill activates the relevant rules and generates both positive and negative prompts in a single LLM call, followed by only one T2V generation, without test-time visual search or backbone fine-tuning. Evolved using only 100 prompts over 14 hours, TIDE achieves prompt-optimization performance comparable to computationally expensive search-based methods. Moreover, when transferred to previously unseen video-generation models, TIDE outperforms existing approaches specifically designed or trained for those models. Code is available at https://github.com/xiningin/TIDE_T2V.
Chengxing Zhou, Yuliang Huang, Jingwei Song
Sun Yat-sen University, China · The University of Hong Kong, China