Learning Physics-Grounded 4D Dynamics with Neural Gaussian Force Fields

• URL: http://arxiv.org/abs/2602.00148v1
• Date: Thu, 29 Jan 2026 11:37:41 GMT
• Title: Learning Physics-Grounded 4D Dynamics with Neural Gaussian Force Fields
• Authors: Shiqian Li, Ruihong Shen, Junfeng Ni, Chang Pan, Chi Zhang, Yixin Zhu,
• Abstract summary: We introduce an end-to-end neural framework that integrates 3D Gaussian perception with physics-based dynamic modeling to generate physically realistic 4D videos.<n>We also present GSCollision, a 4D Gaussian dataset featuring diverse materials, multi-object interactions, and complex scenes, totaling over 640k rendered physical videos.
• Score: 11.212256115568772
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Predicting physical dynamics from raw visual data remains a major challenge in AI. While recent video generation models have achieved impressive visual quality, they still cannot consistently generate physically plausible videos due to a lack of modeling of physical laws. Recent approaches combining 3D Gaussian splatting and physics engines can produce physically plausible videos, but are hindered by high computational costs in both reconstruction and simulation, and often lack robustness in complex real-world scenarios. To address these issues, we introduce Neural Gaussian Force Field (NGFF), an end-to-end neural framework that integrates 3D Gaussian perception with physics-based dynamic modeling to generate interactive, physically realistic 4D videos from multi-view RGB inputs, achieving two orders of magnitude faster than prior Gaussian simulators. To support training, we also present GSCollision, a 4D Gaussian dataset featuring diverse materials, multi-object interactions, and complex scenes, totaling over 640k rendered physical videos (~4 TB). Evaluations on synthetic and real 3D scenarios show NGFF's strong generalization and robustness in physical reasoning, advancing video prediction towards physics-grounded world models.

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