Geometry-Guided Camera Motion Understanding in VideoLLMs
Camera motion is a fundamental geometric signal that shapes visual perception and cinematic style. However, despite its importance, current video-capable vision-language models (VideoLLMs) rarely represent camera motion explicitly. This can lead to a failure in recognizing fine-grained motion primitives. The recent work titled “Geometry-Guided Camera Motion Understanding in VideoLLMs” aims to address this significant gap in the field.
Key Contributions
The authors present a comprehensive framework that includes three main components: benchmarking, diagnosis, and injection. This innovative approach is designed to enhance the understanding of camera motion in VideoLLMs. The key contributions are as follows:
- CameraMotionDataset: A large-scale synthetic dataset curated with explicit camera control, providing a robust foundation for evaluating camera motion understanding.
- Constraint-aware Multi-label Recognition: The formulation of camera motion as a recognition task that is aware of various constraints, enabling more accurate identification of motion primitives.
- CameraMotionVQA Benchmark: A new Visual Question Answering (VQA) benchmark that assesses the ability of models to understand and respond to questions related to camera motion.
Findings from Experiments
Across various off-the-shelf VideoLLMs, substantial errors were observed in recognizing camera motion primitives. Probing experiments conducted on the Qwen2.5-VL vision encoder revealed that camera motion cues are weakly represented, particularly in deeper Vision Transformer (ViT) blocks. This finding helps explain the failure modes previously identified in these models.
Proposed Solution
To bridge the gap in camera motion understanding without the need for costly training or fine-tuning, the authors propose a lightweight, model-agnostic pipeline. This innovative solution includes the following steps:
- Extraction of Geometric Camera Cues: Utilizing 3D foundation models (3DFMs) to extract essential geometric camera cues.
- Prediction of Constrained Motion Primitives: Implementing a temporal classifier to predict motion primitives based on the extracted cues.
- Injection into VideoLLM Inference: Integrating the predicted motion information into downstream VideoLLM inference through structured prompting.
Results and Implications
Experiments demonstrated a significant improvement in motion recognition and produced more camera-aware model responses. The results highlight the effectiveness of geometry-driven cue extraction and structured prompting as practical steps toward achieving a camera-aware VideoLLM and Visual Language Architecture (VLA) system.
Availability of Resources
The CameraMotionDataset and the CameraMotionVQA benchmark are publicly available at the following link: Camera Motion Dataset and Benchmark. This resource aims to foster further research and development in the area of camera motion understanding within VideoLLMs.