Geometry-Guided Camera Motion Understanding in VideoLLMs
📰 ArXiv cs.AI
Geometry-Guided Camera Motion Understanding improves VideoLLMs with a framework of benchmarking, diagnosis, and injection
Action Steps
- Curate a large-scale synthetic dataset like CameraMotionDataset with explicit camera motion annotations
- Benchmark current VideoLLMs on this dataset to identify their limitations
- Diagnose the failures of VideoLLMs on fine-grained motion primitives
- Inject geometric guidance into VideoLLMs to improve their understanding of camera motion
Who Needs to Know This
Computer vision engineers and researchers on a team benefit from this framework as it enhances the understanding of camera motion in VideoLLMs, while product managers can apply this to improve video analysis and generation products
Key Insight
💡 Explicit representation of camera motion is crucial for VideoLLMs to understand visual perception and cinematic style
Share This
💡 Improve VideoLLMs with geometry-guided camera motion understanding
Key Takeaways
Geometry-Guided Camera Motion Understanding improves VideoLLMs with a framework of benchmarking, diagnosis, and injection
Full Article
Title: Geometry-Guided Camera Motion Understanding in VideoLLMs
Abstract:
arXiv:2603.13119v2 Announce Type: replace-cross Abstract: Camera motion is a fundamental geometric signal that shapes visual perception and cinematic style, yet current video-capable vision-language models (VideoLLMs) rarely represent it explicitly and often fail on fine-grained motion primitives. We address this gap with a framework of $\textbf{benchmarking}$, $\textbf{diagnosis}$, and $\textbf{injection}$. We curate $\textbf{CameraMotionDataset}$, a large-scale synthetic dataset with explicit
Abstract:
arXiv:2603.13119v2 Announce Type: replace-cross Abstract: Camera motion is a fundamental geometric signal that shapes visual perception and cinematic style, yet current video-capable vision-language models (VideoLLMs) rarely represent it explicitly and often fail on fine-grained motion primitives. We address this gap with a framework of $\textbf{benchmarking}$, $\textbf{diagnosis}$, and $\textbf{injection}$. We curate $\textbf{CameraMotionDataset}$, a large-scale synthetic dataset with explicit
DeepCamp AI