以帧思考：基于帧奖励模型的生成视频失真评估
Thinking with Frames: Generative Video Distortion Evaluation via Frame Reward Model
为视频生成提供帧级奖励模型与失真归因标签，可直接作为 T2V 后训练（如 GRPO 强化学习）的奖励信号，或用于诊断生成视频中的物体外观与交互异常；其动态采样机制可复用于长视频中稀疏失真帧的高效定位。
原文依据 we introduce REACT, a frame-level reward model designed specifically for structural distortions evaluation in generative videos
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论近期视频奖励模型与后训练策略的进展提升了文本到视频（T2V）生成的质量。然而，这些模型通常只评估视觉质量、运动质量和文本对齐，往往忽略了关键的结构性失真，例如异常的物体外观与交互，而这些失真会降低生成视频的整体质量。为弥补这一空白，我们提出 REACT，一个专为生成视频中的结构性失真评估而设计的帧级奖励模型。REACT 通过对视频帧进行推理来分配逐点分数与归因标签，重点在于识别失真。为支撑这一目标，我们构建了一个大规模人类偏好数据集，其标注基于我们提出的结构性失真分类体系，并利用高效的思维链（CoT）合成流程生成额外数据。REACT 采用两阶段框架训练：（1）带掩码损失的监督微调以注入领域知识，随后（2）使用群体相对策略优化（GRPO）与成对奖励进行强化学习，以增强推理能力并使输出分数与人类偏好对齐。在推理阶段，我们引入动态采样机制，以聚焦于最可能出现失真的帧。我们还提出了 REACT-Bench，一个用于生成视频失真评估的基准。实验结果表明，REACT 在评估结构性失真方面与现有奖励模型形成互补，同时实现了准确的定量评估与可解释的归因分析。
英文原文摘要 1578 字符
Recent advances in video reward models and post-training strategies have improved text-to-video (T2V) generation. While these models typically assess visual quality, motion quality, and text alignment, they often overlook key structural distortions, such as abnormal object appearances and interactions, which can degrade the overall quality of the generative video.To address this gap, we introduce REACT, a frame-level reward model designed specifically for structural distortions evaluation in generative videos. REACT assigns point-wise scores and attribution labels by reasoning over video frames, focusing on recognizing distortions. To support this, we construct a large-scale human preference dataset, annotated based on our proposed taxonomy of structural distortions, and generate additional data using a efficient Chain-of-Thought (CoT) synthesis pipeline. REACT is trained with a two-stage framework: (1) supervised fine-tuning with masked loss for domain knowledge injection, followed by (2) reinforcement learning with Group Relative Policy Optimization (GRPO) and pairwise rewards to enhance reasoning capability and align output scores with human preferences. During inference, a dynamic sampling mechanism is introduced to focus on frames most likely to exhibit distortion.We also present REACT-Bench, a benchmark for generative video distortion evaluation. Experimental results demonstrate that REACT complements existing reward models in assessing structural distortion, achieving both accurate quantitative evaluations and interpretable attribution analysis.