PhysGaia：面向动态新视角合成的多体交互物理感知基准
PhysGaia: A Physics-aware Benchmark with Multi-Body Interactions for Dynamic Novel View Synthesis
为动态新视角合成提供带3D粒子轨迹与物理参数（如粘度）真值的物理一致性评测基准，可直接用于检验4D高斯泼溅等动态重建方法是否遵守物理规律，并推动物理仿真与深度学习结合的场景理解研究。
原文依据 We introduce PhysGaia, a novel physics-aware benchmark for Dynamic Novel View Synthesis (DyNVS)
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论我们提出 PhysGaia，一个面向动态新视角合成（DyNVS）的新型物理感知基准，涵盖结构化物体与非结构化物理现象。现有数据集主要关注照片级真实外观，而 PhysGaia 专门用于支持物理一致的动态重建。该基准包含具有丰富多体交互的复杂场景，其中物体真实地碰撞并交换力。此外，它涵盖多种材料，包括液体、气体、织物和流变物质，超越了此前工作中普遍存在的刚体假设。为确保物理保真度，PhysGaia 中的所有场景均使用严格遵循基本物理定律的材料特定物理求解器生成。我们提供全面的真值信息，包括三维粒子轨迹和物理参数（如粘度），从而支持对物理建模的定量评估。为便于研究采用，我们还提供了近期 4D 高斯泼溅模型与本数据集及其结果的集成流程。通过弥补物理感知基准的严重短缺，PhysGaia 能够显著推进动态视角合成、基于物理的场景理解以及深度学习与物理仿真融合的研究，最终实现对复杂动态场景更忠实的重建与解读。
英文原文摘要 1475 字符
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.