CameraBench is a benchmark and research project for evaluating camera motion understanding in videos, comparing classical Structure-from-Motion (SfM) methods and modern Vision-Language Models (VLMs). The repository serves as a landing page with download scripts for the test dataset (videos and labels from Hugging Face), a leaderboard showing model performance, and links to fine-tuned Qwen2.5-VL models. The actual evaluation and training code resides in a separate repository (t2v_metrics). The repo lacks a self-contained pipeline, dependency management, or data processing scripts; it is essentially a static site with download helpers. A notebook template for a ReLU activation function exercise is included but is unrelated to the core project. The benchmark is novel and published at NeurIPS 2025, but the codebase itself is minimal and not reusable beyond data retrieval.