RAPO++: Cross-Stage Prompt Optimization for Text-to-Video Generation via
  Data Alignment and Test-Time Scaling
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 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 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. 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. 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
https://github.com/Vchitect/RAPO.
68fee03d6cdff8b857f47181
Bingjie Gao
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2025-10-27T10:27:06.672Z
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gao
bingjie
user
bingjie
68fee03d6cdff8b857f47182
Qianli Ma
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2025-10-27T10:27:15.362Z
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Qianli Ma
Mqleet
user
Mqleet
68fee03d6cdff8b857f47183
Xiaoxue Wu
68fee03d6cdff8b857f47184
Shuai Yang
68fee03d6cdff8b857f47185
Guanzhou Lan
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2025-10-27T10:27:03.687Z
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Guanzhou Lan
Guanzhou111
user
Guanzhou111
68fee03d6cdff8b857f47186
Haonan Zhao
68fee03d6cdff8b857f47187
Jiaxuan Chen
68fee03d6cdff8b857f47188
Qingyang Liu
68fee03d6cdff8b857f47189
Yu Qiao
68fee03d6cdff8b857f4718a
Xinyuan Chen
68fee03d6cdff8b857f4718b
Yaohui Wang
68fee03d6cdff8b857f4718c
Li Niu
RAPO++: Cross-Stage Prompt Optimization for Text-to-Video Generation via
  Data Alignment and Test-Time Scaling
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 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 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. 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. 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
https://github.com/Vchitect/RAPO.
RAPO++ enhances text-to-video generation by optimizing user prompts through retrieval, iterative refinement, and LLM fine-tuning, improving semantic alignment, compositionality, and temporal coherence.
diffusion-based T2V models cross-stage prompt optimization Retrieval-Augmented Prompt Optimization (RAPO) Sample-Specific Prompt Optimization (SSPO) large language model (LLM) fine-tuning relation graph semantic alignment spatial fidelity temporal coherence optical flow compositional reasoning temporal stability physical plausibility