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，一种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。现有数据集主要聚焦于光度逼真外观，而PhysGaia专为支持物理一致性动态重建而设计。该基准以丰富多体相互作用的复杂场景为特色，其中物体以符合物理规律的方式碰撞并交换作用力。进一步地，它整合了液体、气体、纺织品与流变物质等多种材质，突破了先前工作中普遍存在的刚体假设。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专属物理求解器生成。我们提供全面的真值信息，包括三维粒子轨迹与物理参数（如粘度），从而支持对物理建模的定量评估。为便于研究应用，我们还提供了面向近期4D高斯泼溅模型的集成流程、数据集及其结果。通过填补物理感知基准的关键空白，PhysGaia可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，最终实现对复杂动态场景更精确的重建与解译。