Beyond Static Vision: Scene Dynamic Field Unlocks Intuitive Physics Understanding in Multi-modal Large Language Models
Summary: arXiv:2604.03302v1 Announce Type: cross
Abstract
While Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in image and video understanding, their ability to comprehend the physical world has become an increasingly important research focus. Despite their improvements, current MLLMs struggle significantly with high-level physics reasoning. In this work, we investigate the first step of physical reasoning, i.e., intuitive physics understanding, revealing substantial limitations in understanding the dynamics of continuum objects.
Key Findings
To isolate and evaluate this specific capability, we introduce two fundamental benchmark tasks:
- Next Frame Selection (NFS): A task designed to assess the ability of MLLMs to predict subsequent frames in dynamic environments.
- Temporal Coherence Verification (TCV): A task aimed at testing the consistency of physical interactions over time.
Our experiments demonstrate that even state-of-the-art MLLMs perform poorly on these foundational tasks, indicating a critical gap in their capabilities.
Proposed Solution
To address this limitation, we propose the Scene Dynamic Field (SDF), a concise approach that leverages physics simulators within a multi-task fine-tuning framework. The SDF approach substantially improves performance, achieving up to 20.7% gains on fluid tasks while showing strong generalization to unseen physical domains.
Importance of this Research
This work not only highlights a critical gap in current MLLMs but also presents a promising cost-efficient approach for developing more physically grounded MLLMs. By integrating intuitive physics understanding into the framework of MLLMs, we enhance their overall capabilities in dealing with dynamic environments.
Conclusion
The findings underscore the necessity for ongoing research in intuitive physics understanding within the realm of MLLMs. Our proposed Scene Dynamic Field serves as a foundation for future advancements, paving the way for more sophisticated models that can accurately interpret and predict physical interactions.
For those interested, our code and data are available at https://github.com/andylinx/Scene-Dynamic-Field.