MotionBench: Benchmarking and Improving Fine-grained Video Motion Understanding for Vision Language Models

Understand

In recent years, vision language models (VLMs) have made significant advancements in video understanding.

• However, a crucial capability - fine-grained motion comprehension - remains under-explored in current benchmarks.
• To address this gap, we propose MotionBench, a comprehensive evaluation benchmark designed to assess the fine-grained motion comprehension of video understanding models.
• MotionBench evaluates models' motion-level perception through six primary categories of motion-oriented question types and includes data collected from diverse sources, ensuring a broad representation of real-world video content.