For vision-language models (VLMs), understanding the dynamic properties of objects and their interactions within 3D scenes from video is crucial for effective reasoning. In this work, we introduce a video question answering dataset SuperCLEVR-Physics that focuses on the dynamics properties of objects. We concentrate on physical concepts -- velocity, acceleration, and collisions within 4D scenes, where the model needs to fully understand these dynamics properties and answer the questions built on top of them. From the evaluation of a variety of current VLMs, we find that these models struggle with understanding these dynamic properties due to the lack of explicit knowledge about the spatial structure in 3D and world dynamics in time variants. To demonstrate the importance of an explicit 4D dynamics representation of the scenes in understanding world dynamics, we further propose NS-4Dynamics, a Neural-Symbolic model for reasoning on 4D Dynamics properties under explicit scene representation from videos. Using scene rendering likelihood combining physical prior distribution, the 4D scene parser can estimate the dynamics properties of objects over time to and interpret the observation into 4D scene representation as world states. By further incorporating neural-symbolic reasoning, our approach enables advanced applications in future prediction, factual reasoning, and counterfactual reasoning. Our experiments show that our NS-4Dynamics suppresses previous VLMs in understanding the dynamics properties and answering questions about factual queries, future prediction, and counterfactual reasoning. Moreover, based on the explicit 4D scene representation, our model is effective in reconstructing the 4D scenes and re-simulate the future or counterfactual events.

该研究提出了一个基于视频的问答数据集SuperCLEVR-Physics，专注于对象的动力学特性，通过显式的4D场景表示和神经符号推理，展示了NS-4Dynamics在理解动力学属性以及未来预测和反事实推理方面的效果，该模型在重建4D场景并重新模拟未来或反事实事件方面表现良好。

基于物理先验的组合式4D动态场景理解与视频问答