Synthetic Vision: Training Vision-Language Models to Understand Physics (2412.08619v1)

Abstract: Physical reasoning, which involves the interpretation, understanding, and prediction of object behavior in dynamic environments, remains a significant challenge for current Vision-LLMs (VLMs). In this work, we propose two methods to enhance VLMs' physical reasoning capabilities using simulated data. First, we fine-tune a pre-trained VLM using question-answer (QA) pairs generated from simulations relevant to physical reasoning tasks. Second, we introduce Physics Context Builders (PCBs), specialized VLMs fine-tuned to create scene descriptions enriched with physical properties and processes. During physical reasoning tasks, these PCBs can be leveraged as context to assist a LLM to improve its performance. We evaluate both of our approaches using multiple benchmarks, including a new stability detection QA dataset called Falling Tower, which includes both simulated and real-world scenes, and CLEVRER. We demonstrate that a small QA fine-tuned VLM can significantly outperform larger state-of-the-art foundational models. We also show that integrating PCBs boosts the performance of foundational LLMs on physical reasoning tasks. Using the real-world scenes from the Falling Tower dataset, we also validate the robustness of both approaches in Sim2Real transfer. Our results highlight the utility that simulated data can have in the creation of learning systems capable of advanced physical reasoning.

Enhancing Physical Reasoning in Vision-LLMs Through Synthetic Data

The paper "Synthetic Vision: Training Vision-LLMs to Understand Physics" addresses the persistent challenge of integrating physical reasoning capabilities into Vision-LLMs (VLMs). By introducing two distinct methodologies predicated on simulated data, the authors aim to augment VLMs such that they can more effectively interpret, understand, and predict object behavior in dynamic environments, a task that has eluded many high-performing models.

Methodology Overview

The authors propose two distinct but complementary methods designed to improve the physical reasoning capacities of VLMs:

1. QA-based Fine-Tuning: This approach centers around utilizing question-answer (QA) pairs generated from simulations that are specifically designed to reflect relevant physical reasoning tasks. By fine-tuning pre-existing VLMs with these QA pairs, especially those derived from novel datasets like the Falling Tower, the models can be tailored to embody enhanced physical understanding. This fine-tuning process is facilitated through Low-Rank Adaptation (LoRA), which efficiently updates a limited set of parameters.
2. Physics Context Builders (PCBs): PCBs are designed to provide enriched scene descriptions that incorporate physical properties and processes. These specialized VLMs serve as a context provider when integrated with LLMs in a multi-agent framework. This setup allows foundational LLMs, such as GPT-4o and Gemini, to leverage detailed visual physics priors generated by PCBs, thus optimizing their reasoning performance without needing extensive retraining.

Experimental Validation

The researchers validate their methods on an array of benchmarks:

• Falling Tower Dataset: This new dataset, similar in scope to ShapeStacks, includes simulated scenes along with detailed QA pairs. It serves as an optimal testbed for evaluating an agent's reasoning regarding the stability of object stacks. The results highlight that the fine-tuned VLMs markedly outperform larger state-of-the-art models in both descriptive and predictive tasks. Moreover, their robustness is confirmed in Sim2Real transfer using real-world captured data.
• CLEVRER Dataset: Enabling a test of dynamic physics reasoning, CLEVRER comprises synthetic videos and associated QAs. Here, fine-tuned VLMs again demonstrate enhanced descriptive, explanatory, and counterfactual reasoning abilities over zero-shot models. PCBs show moderate success, suggesting promising potential in augmenting LLMs with context-enriched data when handling video-based dynamics.

Implications and Future Directions

The findings underscore the efficacy of using simulation data to enrich the physical reasoning capabilities of VLMs without establishing additional computational requirements during inference, as opposed to simulation-in-the-loop methodologies. This framework can serve as a foundational leap forward, especially in building more sophisticated AI systems capable of performing complex physical reasoning tasks.

Future research could focus on extending the scope of simulated environments to encompass more intricate physical phenomena, including fluid dynamics and multi-body interactions. Furthermore, leveraging synthetic methods to process and interpret unstructured real-world videos remains a promising avenue, potentially leading to broader applicability in practical scenarios. Additionally, there is scope for refining the PCB framework to generate predictive insights that could further enhance model performance on forward-looking tasks.

Conclusion

By presenting QA-based fine-tuning and PCB frameworks, this paper delivers a compelling strategy for advancing the physical reasoning capabilities of VLMs. The results convincingly argue that targeted simulated training exceeds the benefits of mere scale in training data or model size, offering a cogent pathway toward more intelligent, context-aware AI systems.