A Reason-then-Describe Instruction Interpreter for Controllable Video Generation
ReaDe employs a 'reason-then-describe' framework with two-stage training, achieving significant improvements in instruction fidelity and controllable video generation.
Key Findings
Methodology
ReaDe is built on a multimodal large language model, integrating Chain-of-Thought (CoT) reasoning to decompose complex instructions. The first stage involves supervised fine-tuning on a curated dataset with reasoning traces and dense captions, enhancing stepwise interpretability. The second stage employs multi-dimensional reward signals, including content completeness, reasoning coherence, and consistency, optimized via Group Relative Policy Optimization (GRPO). The model processes textual, visual, and audio inputs, producing detailed structured descriptions that guide downstream video generators. This approach ensures robust generalization across single- and multi-condition scenarios, effectively bridging the gap between user input and generation output.
Key Results
- In multi-condition control tasks, ReaDe surpasses baselines with a CLIP-T score of 18.64 (vs. 17.15), depth MAE reduced to 18.79 (vs. 21.87), camera rotation error lowered to 1.30 (vs. 20.16), and human pose accuracy increased to 32.76% (vs. 21.11%). These results demonstrate its superior reasoning and control capabilities, especially in complex scenarios.
- Ablation studies confirm that combining Chain-of-Thought initialization with reinforcement learning via GRPO yields the best performance, with reward design playing a crucial role. The model maintains high generalization ability, performing well on unseen instructions and conditions.
- The multi-dimensional reward framework effectively guides the model to generate more faithful, detailed, and coherent descriptions, significantly improving downstream video fidelity and controllability metrics.
Significance
This work addresses a fundamental bottleneck in controllable video synthesis: translating concise, ambiguous user prompts into detailed, faithful descriptions that can be reliably used by generative models. By explicitly modeling the reasoning process and optimizing with feedback, ReaDe enhances interpretability and robustness, enabling more precise control over complex multi-modal scenarios. The approach bridges the gap between user intent and content generation, facilitating applications in virtual reality, content creation, and human-computer interaction. Its strong generalization across unseen conditions marks a step toward truly intelligent, adaptable content synthesis systems, with broad implications for AI-driven media production.
Technical Contribution
The paper introduces a novel 'reason-then-describe' framework that explicitly models the reasoning process behind instruction interpretation. It combines Chain-of-Thought reasoning with multi-dimensional reward-based reinforcement learning, leveraging GRPO for stable policy updates. The model architecture integrates multimodal inputs—text, images, audio, and camera signals—into a unified large language model backbone. The training pipeline includes supervised fine-tuning on a curated dataset with reasoning traces and dense captions, followed by reinforcement learning to optimize content fidelity, coherence, and generalization. This comprehensive approach surpasses prior methods by explicitly capturing the reasoning process and enabling multi-condition, cross-modal generalization.
Novelty
This work is the first to embed explicit reasoning—via Chain-of-Thought—into the instruction interpretation for controllable video generation. Unlike prior approaches relying solely on end-to-end training or rule-based parsing, ReaDe models the stepwise reasoning process, enabling better handling of complex, multi-modal instructions. Its multi-dimensional reward mechanism and use of GRPO for stable optimization further distinguish it from existing methods, providing a robust, generalizable framework that effectively bridges user prompts and high-fidelity video outputs.
Limitations
- Despite strong performance, the model may still struggle with highly ambiguous or extremely complex instructions, leading to reasoning errors that affect output fidelity.
- Training relies on large, high-quality multimodal datasets, which are costly and labor-intensive to curate.
- The inference process involves multiple reasoning steps, which can introduce computational overhead, limiting real-time applications.
Future Work
Future directions include enhancing the efficiency of reasoning processes, integrating user feedback for interactive refinement, and extending the framework to incorporate additional modalities like speech and haptic signals. Further research will focus on reducing data dependency, improving interpretability, and deploying in real-time systems for practical applications such as live video editing and interactive content creation.
AI Executive Summary
Controllable video generation has seen rapid progress with models like Diffusion Transformers, which produce high-fidelity, temporally coherent videos. However, aligning user intentions with generated content remains a challenge, especially when user inputs are concise or ambiguous. Existing methods often rely on detailed prompts during training, but real-world inputs are typically short, leading to a mismatch that hampers controllability and quality.
To address this, the paper introduces ReaDe, a universal instruction interpreter that adopts a 'reason-then-describe' paradigm inspired by Chain-of-Thought reasoning. ReaDe first analyzes user instructions to identify core requirements and resolve ambiguities, then generates detailed, structured descriptions that guide downstream video generators. The model is trained in two stages: supervised fine-tuning on a curated dataset with reasoning traces and dense captions, followed by reinforcement learning with a multi-dimensional reward system. This reward framework evaluates content completeness, reasoning coherence, and consistency, ensuring high-quality, faithful descriptions.
The core technical innovation lies in combining explicit reasoning with multi-modal inputs—text, images, audio, and camera signals—and optimizing with Group Relative Policy Optimization (GRPO) for stability and generalization. Extensive experiments demonstrate that ReaDe outperforms baseline methods across single- and multi-condition control scenarios, achieving higher instruction fidelity, caption accuracy, and video quality metrics. Notably, the model generalizes well to unseen instructions, maintaining robust performance.
This work significantly advances the field of controllable video synthesis by bridging the gap between user intent and content generation. Its explicit reasoning framework enhances interpretability and control, paving the way for more intelligent, flexible content creation tools. Future work will focus on improving inference speed, expanding multimodal capabilities, and enabling real-time interactive applications, ultimately transforming how AI-driven media content is produced and consumed.
Deep Analysis
Background
近年来，视频生成技术快速发展，Diffusion Transformer（Peebles & Xie, 2023）等模型显著提升了生成内容的细节还原和时间一致性。早期研究主要关注文本控制（Singer et al., 2022），随着用户需求多样化，细粒度条件（如图像、姿态、深度）被引入以增强控制能力（Wang et al., 2024）。然而，训练依赖详细提示，实际应用中用户输入多为简短模糊，导致模型难以准确理解意图，影响生成效果。为此，研究逐步转向指令解释和重述（Chen et al., 2024），试图将用户原始输入转化为详细描述以改善控制效果（Wu et al., 2025）。但现有方法多为模型特定或缺乏推理能力，难以应对复杂、多模态和推理驱动的场景，存在泛化不足的问题。
Core Problem
核心问题在于如何将用户的简短、模糊指令转化为详细、结构化的描述，确保生成内容忠实用户意图。现有方法多依赖大规模数据训练或规则匹配，难以应对复杂推理和少样本场景，导致输出偏差和控制不精确。这一瓶颈限制了视频生成的实用性和用户体验，亟需一种具有推理能力、泛化强的指令理解框架。
Innovation
本研究提出“推理-描述”范式，结合Chain-of-Thought（Wei et al., 2022）推理机制，显式建模用户指令的推理过程，逐步解析核心需求。创新点包括：
- �� 多模态大语言模型，融合文本、视觉、音频信息，提升理解能力；
- �� 两阶段训练：第一阶段通过推理增强的监督学习，第二阶段利用多维奖励进行强化优化；
- �� 引入Group Relative Policy Optimization（Guo et al., 2025），实现稳定高效的策略优化，增强泛化能力；
- �� 构建多条件、多模态训练数据，提升模型在复杂场景中的适应性。
Methodology
- �� 输入：用户短促指令，结合视觉、音频和摄像机信息；
- �� 第一阶段：利用推理增强的监督数据（包括推理轨迹和密集描述）微调模型，赋予逐步推理能力；
- �� 逐步推理：解析用户意图、识别模糊信息、对多模态信息进行对齐，形成推理链；
- �� 生成结构化密集描述：整合多模态信息，输出详细的场景描述、动作、风格、视角等；
- �� 第二阶段：设计多维奖励（内容完整性、推理合理性、描述一致性），结合强化学习（GRPO）优化模型策略；
- �� 训练过程中，构建多条件、多模态数据集，利用奖励信号引导模型学习复杂指令的理解与描述。
Experiments
采用包含多模态条件（如深度、姿态、摄像机运动等）的8.4K训练样本，评估在单条件和多条件控制任务中的表现。对比基线方法（如Any2Caption），指标包括CLIP-T、深度MAE、旋转误差等。模型参数调优采用余弦调度，强化学习使用固定学习率（2.5e-6）和多轮采样。通过消融实验验证推理引导和奖励设计的效果，测试模型在未见条件下的泛化能力。
Results
ReaDe在多条件控制任务中表现优异，CLIP-T指标达18.64，深度MAE降至18.79，旋转误差降低至1.30，人类姿态准确率提升至32.76%。在复杂组合场景（如摄像机+深度+多身份）中，性能优于对比模型，显示出强大的推理和泛化能力。奖励机制的引入显著提升了描述的结构化和内容完整性，验证了“推理-描述”策略的有效性。
Applications
该方法适用于虚拟内容创作、虚拟现实、交互式动画等场景，用户只需提供简短指令，模型即可生成符合预期的高质量视频。未来可结合用户交互，动态调整指令理解，提升个性化和实时性，推动智能内容生成产业升级。
Limitations & Outlook
模型在极端复杂或模糊指令下仍可能出现推理偏差，影响生成效果。训练依赖大量多模态标注数据，成本较高。推理过程较复杂，实时应用存在计算瓶颈。未来需优化推理效率和模型解释能力。
Abstract
Diffusion Transformers have significantly improved video fidelity and temporal coherence, however, practical controllability remains limited. Concise, ambiguous, and compositionally complex user inputs contrast with the detailed prompts used in training, yielding an intent-output mismatch. We propose ReaDe, a universal, model-agnostic interpreter that converts raw instructions into precise, actionable specifications for downstream video generators. ReaDe follows a reason-then-describe paradigm: it first analyzes the user request to identify core requirements and resolve ambiguities, then produces detailed guidance that enables faithful, controllable generation. We train ReaDe via a two-stage optimization: (i) reasoning-augmented supervision imparts analytic parsing with stepwise traces and dense captions, and (ii) a multi-dimensional reward assigner enables stable, feedback-driven refinement for natural-style captions. Experiments across single- and multi-condition scenarios show consistent gains in instruction fidelity, caption accuracy, and downstream video quality, with strong generalization to reasoning-intensive and unseen inputs. ReaDe offers a practical route to aligning controllable video generation with accurately interpreted user intent. Project Page: https://sqwu.top/ReaDe/.