Natural Language Camera Movement Understanding

Understanding camera movement in natural language is crit-ical for training and evaluating video generation models, among other ap-plications. However, we demonstrate that existing vision-language mod-els (VLMs) fail this task in surprising ways, frequently confusing trans-lation with rotation, left with right, and object movement with cameramovement. To address these limitations, we establish natural languagecamera movement understanding as a standalone research task. We in-troduce a two-level cinematographic taxonomy and an extensive, atomicbenchmark featuring both real and synthetic videos. Furthermore, we cu-rate a large-scale, multi-source training set enhanced by targeted cameramovement augmentation. Our fine-tuned VLM-8B outperforms Gemini3.1 Pro by 10% and 11% on our benchmark’s real and synthetic videos,respectively. Despite these gains, a significant gap remains relative to hu-man performance, underscoring the need to promote and facilitate futureresearch on natural language camera movement understanding.