We introduce PhysGaia, a novel physics-aware benchmark for Dynamic Novel View Synthesis (DyNVS) that encompasses both structured objects and unstructured physical phenomena. While existing datasets primarily focus on photorealistic appearance, PhysGaia is specifically designed to support physics-consistent dynamic reconstruction. Our benchmark features complex scenarios with rich multi-body interactions, where objects realistically collide and exchange forces. Furthermore, it incorporates a diverse range of materials, including liquid, gas, textile, and rheological substance, moving beyond the rigid-body assumptions prevalent in prior work. To ensure physical fidelity, all scenes in PhysGaia are generated using material-specific physics solvers that strictly adhere to fundamental physical laws. We provide comprehensive ground-truth information, including 3D particle trajectories and physical parameters (e.g., viscosity), enabling the quantitative evaluation of physical modeling. To facilitate research adoption, we also provide integration pipelines for recent 4D Gaussian Splatting models along with our dataset and their results. By addressing the critical shortage of physics-aware benchmarks, PhysGaia can significantly advance research in dynamic view synthesis, physics-based scene understanding, and the integration of deep learning with physical simulation, ultimately enabling more faithful reconstruction and interpretation of complex dynamic scenes.

翻译：我们提出PhysGaia——一个面向动态新视角合成（DyNVS）的新型物理感知基准，其涵盖结构化物体与非结构化物理现象。不同于现有数据集主要关注照片级真实感外观，PhysGaia专为支持物理一致性的动态重建而设计。本基准包含具有丰富多体交互的复杂场景，其中物体以符合物理规律的方式相互碰撞并交换作用力。此外，该基准突破了以往工作中普遍存在的刚体假设，纳入了液体、气体、纺织品及流变材料等多样化材质。为确保物理保真度，PhysGaia中所有场景均采用严格遵循基本物理定律的材质特定物理求解器生成。我们提供包括3D粒子轨迹和物理参数（如黏度）在内的完整真值信息，从而支持物理建模的定量评估。为促进研究应用，我们还提供了与最新4D高斯溅射模型的集成管线，以及数据集与对应结果。通过填补物理感知基准的严重缺失，PhysGaia能够显著推动动态视角合成、基于物理的场景理解以及深度学习与物理模拟的融合研究，最终实现更忠实的复杂动态场景重建与解析。