PhysGaia: A Physics-Aware Benchmark with Multi-Body Interactions for Dynamic Novel View Synthesis
Mijeong Kim, Gunhee Kim, Jungyoon Choi, Wonjae Roh, Bohyung Han

TL;DR
PhysGaia is a physics-aware benchmark dataset for dynamic novel view synthesis that includes complex multi-body interactions and diverse materials, supporting physics-consistent scene reconstruction.
Contribution
It introduces a new benchmark with physics-based scene generation, comprehensive ground-truth data, and integration tools for advanced dynamic view synthesis research.
Findings
Supports physics-consistent dynamic scene reconstruction
Includes diverse materials like liquids and gases
Provides ground-truth physical parameters and trajectories
Abstract
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…
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