PhysGaia: A Physics-Aware Dataset of Multi-Body Interactions for Dynamic Novel View Synthesis

• URL: http://arxiv.org/abs/2506.02794v1
• Date: Tue, 03 Jun 2025 12:19:18 GMT
• Title: PhysGaia: A Physics-Aware Dataset of Multi-Body Interactions for Dynamic Novel View Synthesis
• Authors: Mijeong Kim, Gunhee Kim, Jungyoon Choi, Wonjae Roh, Bohyung Han,
• Abstract summary: PhysGaia is a physics-aware dataset specifically designed for Dynamic Novel View Synthesis (DyNVS)<n>Our dataset provides complex dynamic scenarios with rich interactions among multiple objects.<n>PhysGaia will significantly advance research in dynamic view synthesis, physics-based scene understanding, and deep learning models integrated with physical simulation.
• Score: 62.283499219361595
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We introduce PhysGaia, a novel physics-aware dataset specifically designed for Dynamic Novel View Synthesis (DyNVS), encompassing both structured objects and unstructured physical phenomena. Unlike existing datasets that primarily focus on photorealistic reconstruction, PhysGaia is created to actively support physics-aware dynamic scene modeling. Our dataset provides complex dynamic scenarios with rich interactions among multiple objects, where they realistically collide with each other and exchange forces. Furthermore, it contains a diverse range of physical materials, such as liquid, gas, viscoelastic substance, and textile, which moves beyond the rigid bodies prevalent in existing datasets. All scenes in PhysGaia are faithfully generated to strictly adhere to physical laws, leveraging carefully selected material-specific physics solvers. To enable quantitative evaluation of physical modeling, our dataset provides essential ground-truth information, including 3D particle trajectories and physics parameters, e.g., viscosity. To facilitate research adoption, we also provide essential integration pipelines for using state-of-the-art DyNVS models with our dataset and report their results. By addressing the critical lack of datasets for physics-aware modeling, PhysGaia will significantly advance research in dynamic view synthesis, physics-based scene understanding, and deep learning models integrated with physical simulation -- ultimately enabling more faithful reconstruction and interpretation of complex dynamic scenes. Our datasets and codes are available in the project website, http://cvlab.snu.ac.kr/research/PhysGaia.

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