Abstract
Prompt design plays a crucial role in text-to-video (T2V) generation, yet user-provided prompts are often short, unstructured, and misaligned with training data, limiting the generative potential of diffusion-based T2V models. We present \textbf{RAPO++}, a cross-stage prompt optimization framework that unifies training-data--aligned refinement, test-time iterative scaling, and large language model (LLM) fine-tuning to substantially improve T2V generation without modifying the underlying generative backbone. In \textbf{Stage 1}, Retrieval-Augmented Prompt Optimization (RAPO) enriches user prompts with semantically relevant modifiers retrieved from a relation graph and refactors them to match training distributions, enhancing compositionality and multi-object fidelity. \textbf{Stage 2} introduces Sample-Specific Prompt Optimization (SSPO), a closed-loop mechanism that iteratively refines prompts using multi-source feedback -- including semantic alignment, spatial fidelity, temporal coherence, and task-specific signals such as optical flow -- yielding progressively improved video generation quality. \textbf{Stage 3} leverages optimized prompt pairs from SSPO to fine-tune the rewriter LLM, internalizing task-specific optimization patterns and enabling efficient, high-quality prompt generation even before inference. Extensive experiments across five state-of-the-art T2V models and five benchmarks demonstrate that RAPO++ achieves significant gains in semantic alignment, compositional reasoning, temporal stability, and physical plausibility, outperforming existing methods by large margins. Our results highlight RAPO++ as a model-agnostic, cost-efficient, and scalable solution that sets a new standard for prompt optimization in T2V generation. The code is available at this https URL.
Abstract (translated)
提示设计在文本到视频（T2V）生成中扮演着关键角色，但用户提供的提示往往简短、结构松散，并且与训练数据不匹配，这限制了基于扩散模型的T2V生成能力。我们提出了\textbf{RAPO++}，这是一个跨阶段提示优化框架，它统一了与训练数据对齐的细化过程、测试时间迭代扩展以及大型语言模型（LLM）微调，以在无需修改底层生成架构的情况下显著提升T2V生成质量。 **第一阶段**：检索增强提示优化（RAPO），通过从关系图中检索语义相关的修饰符来丰富用户提示，并将这些提示重构为与训练分布匹配的形式。这一过程增强了提示的组合性和多对象保真度。 **第二阶段**引入了特定样本提示优化（SSPO），这是一种闭环机制，它迭代地使用包括语义对齐、空间保真度、时间连贯性以及如光流这样的任务特有信号在内的多源反馈来改进提示。这一过程逐步提高了生成视频的质量。 **第三阶段**利用来自SSPO的优化提示对重写LLM进行微调，将特定任务的优化模式内化为模型的一部分，并且即使在推理之前也能高效地生成高质量的提示。 通过五个最先进的T2V模型和五个基准测试所进行的广泛实验表明，RAPO++在语义一致性、组合性推理、时间稳定性以及物理合理性方面取得了显著提升，大幅超越了现有方法。我们的结果强调了RAPO++作为一个与模型无关、成本效益高且可扩展的解决方案的价值，在T2V生成提示优化领域设立了新的标准。 相关代码可在以下链接获取：[https URL]（请将方括号中的内容替换为实际的URL）。
URL
https://arxiv.org/abs/2510.20206
