GAUGE: A Measurement-Grounded Benchmark for Physical Fidelity in Simulation Engines and Video World Models
Ranking
Overall
72
Content
75
Popularity
64
Observed public metrics from 1 member.
Merged summary
TL;DR - GAUGE is a real-world-grounded diagnostic benchmark that jointly measures how faithfully physics engines and generative video world models reproduce actual physics, pinpointing which physical principles or parameters break down rather than relying on perceptual similarity or human judgment.
- 22 controlled task families span rigid bodies, flexible cables, textiles, and volumetric deformables, covering collision, friction, momentum transfer, oscillation, self-contact, and deformation; tasks are grounded in real trajectories with calibrated physical metadata, uncertainty annotations, and task-specific observables.
- Isaac Sim, Genesis, and Newton were benchmarked on 14 task families via generalized trajectory errors; no engine was uniformly faithful, with the largest discrepancies in impulsive contact, rapid textile motion, and volumetric deformation.
- 6 image-to-video models were evaluated on 5 rigid-body tasks for physical-law consistency and temporal stability of inferred parameters; they often produced trajectories with the correct equation form while recovering wrong accelerations, momentum transfer, and oscillation timing.
- Framing: simulators and video world models are evaluated under one measurement-grounded protocol, targeting more physically faithful simulation for embodied intelligence.
Sources (1)
GAUGE: A Measurement-Grounded Benchmark for Physical Fidelity in Simulation Engines and Video World Models
Public signals
Semantic Scholar citations 1 · Semantic Scholar influential citations 0
TL;DR - GAUGE is a real-world-grounded diagnostic benchmark that jointly measures how faithfully physics engines and generative video world models reproduce actual physics, pinpointing which physical principles or parameters break down rather than relying on perceptual similarity or human judgment.
- 22 controlled task families span rigid bodies, flexible cables, textiles, and volumetric deformables, covering collision, friction, momentum transfer, oscillation, self-contact, and deformation; tasks are grounded in real trajectories with calibrated physical metadata, uncertainty annotations, and task-specific observables.
- Isaac Sim, Genesis, and Newton were benchmarked on 14 task families via generalized trajectory errors; no engine was uniformly faithful, with the largest discrepancies in impulsive contact, rapid textile motion, and volumetric deformation.
- 6 image-to-video models were evaluated on 5 rigid-body tasks for physical-law consistency and temporal stability of inferred parameters; they often produced trajectories with the correct equation form while recovering wrong accelerations, momentum transfer, and oscillation timing.
- Framing: simulators and video world models are evaluated under one measurement-grounded protocol, targeting more physically faithful simulation for embodied intelligence.