AI视频生成：关键帧越听话，视频越不自然
现在的AI视频生成工具（如Sora、Runway）允许你上传几张关键帧图片，让AI自动补全中间画面。但最新评测发现：模型越努力还原你给的参考图，生成的视频就越僵硬、不流畅。研究者构建了首个专门测试这个能力的基准KeyFrame-Compass，包含386个样本，覆盖不同场景、关键帧密度等。他们测试了9个主流模型，发现所有模型都存在“忠实执行关键帧”和“自然视频合成”之间的根本矛盾——关键帧越多，视频越像幻灯片。更糟的是，大多数开源模型甚至无法正确理解“故事板”输入（多张图按时间顺序排列），把它们当成独立图片处理。这不是你明天能用的工具，但它揭示了当前技术的天花板：AI在“听指令”和“会创作”之间还无法兼得。
📄 原文摘要(英文)
Video generation increasingly relies on keyframe-based workflows, where creators specify a sequence of reference images to guide generation. Although recent models support multi-keyframe conditioning, it remains unclear whether they can faithfully reproduce the prescribed keyframes while maintaining overall video quality. We present KeyFrame-Compass, the first comprehensive benchmark for evaluating keyframe-conditioned video generation. The benchmark contains 386 carefully curated samples spanning three application domains, two video structures, two prompt granularities, two conditioning formats, and four keyframe densities, enabling controlled analysis under diverse generation settings. We further introduce an automated evaluation framework that jointly measures keyframe execution and overall video quality. Specifically, we decompose keyframe execution into six complementary metrics covering presence, fidelity, temporal ordering, localization, persistence, and uniqueness, while assessing overall video quality through evidence-grounded MLLM judgments augmented with specialized perception models. Experiments on nine representative video generation systems reveal several fundamental limitations. Current models exhibit a clear trade-off between faithful keyframe execution and natural video synthesis. Their performance further degrades as keyframe constraints become denser and most open-source models also fail to interpret storyboard-grid inputs as temporally ordered keyframe sequences.