This episode introduces CameraBench, a large-scale dataset and benchmark designed to improve camera motion understanding in videos. It details a taxonomy of camera motion primitives developed with cinematographers, highlighting how motions can relate to scene content like tracking subjects. The authors describe a rigorous annotation framework and human study demonstrating how domain expertise and training enhance annotation accuracy. Using CameraBench, they evaluate both Structure-from-Motion (SfM) and Video-Language Models (VLMs), finding that SfM struggles with semantic primitives while VLMs struggle with precise geometric motions. Finally, they show that fine-tuning a generative VLM on CameraBench significantly improves performance on tasks like motion-augmented captioning and video question answering.