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For a useful test of an AI-generated video model, combine a broad physics benchmark such as PhyWorldBench or Physics-IQ Verified with a focused diagnostic such as PDI-Bench or PhyProbe. Add GAUGE when you can compare motion against controlled, measured real-world behavior. No single score is a universal grade for physical realism: a clip can look polished and follow its prompt while mishandling motion, geometry, collisions, or cause and effect.
What should a test of physical realism measure?
It should test whether events in a clip behave plausibly, not just whether the images look convincing or depict the requested subject. A video may show the right objects and action yet get gravity, object permanence, collisions, perspective, or the consequences of contact wrong. Visual quality, prompt adherence, and physical plausibility are related but distinct evaluation dimensions.
For example, a generated clip can correctly show a ball being thrown while making its trajectory bend inexplicably, or depict a glass hitting a table without a believable change in velocity. A person may appear anatomically convincing in individual frames while feet slide, limbs stretch, or balance fails over time. A car can remain sharp and coherent while its wheels, suspension, and turning geometry do not agree. These are physical failures even when ordinary viewers describe the clip as realistic.
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That distinction matters when choosing a tool. A prompt-based benchmark can probe many kinds of behavior, a geometry-focused benchmark can examine perspective and structural consistency, and a measurement-grounded benchmark can compare observed dynamics with physical trials. Those approaches answer different questions, so their scores should not be treated as interchangeable.
Which tools are available, and what does each test?
The six resources below cover complementary evaluation needs. The table distinguishes what each project says it evaluates from the practical limits a reader should consider.
| Tool | Best suited to | Evaluation approach and coverage | Key caveat |
|---|---|---|---|
| PhyWorldBench | Broad, prompt-based comparison of text-to-video models | Fundamental and composite physics, including object motion, energy conservation, rigid-body interactions, human and animal motion, and deliberately anti-physics prompts. It describes both human assessment and a zero-shot multimodal-language-model evaluation route. | Prompt set and evaluation route can affect results; report both. |
| Physics-IQ Verified | Testing physical understanding across varied phenomena | Five areas: solid mechanics, fluid dynamics, optics, thermodynamics, and magnetism. The project repository recommends the Verified version. | Keep test examples held out; the project warns against leakage through prompt rewriting or best-of-N selection. |
| PhyProbe | Diagnosing physical violations in an individual clip | Produces a continuous physical-violation score. Its project page says inference takes one video without a prompt or reference pair; a lower score indicates greater consistency on its anchored scale. | It is a learned evaluator, not a universal measure; check its coverage and transfer to the content you care about. |
| PDI-Bench | Geometry and perspective consistency | Examines scale-depth alignment, 3D motion consistency, and 3D structural rigidity. The project page says lower PDI is better. | It focuses on geometry rather than every aspect of physical realism. |
| GAUGE | Measurement-grounded comparisons with real-world behavior | Compares generated or simulated dynamics with controlled experiments using measured trajectories, calibrated parameters, uncertainty estimates, and task-specific observables. | Useful only where measured behavior is available and the benchmark’s task protocols fit the use case. |
| VideoPhy | Physical commonsense alongside caption adherence | Uses human evaluation of clips against real-world activities and physical laws; the authors also introduce an automatic evaluator. | Reported model results belong to the paper’s benchmark and generation setup, not a permanent leaderboard. |
PhyWorldBench: broad coverage for model screening
PhyWorldBench is designed for text-to-video comparisons rather than a single-clip forensic report. Its scenarios include fundamental physics, composite situations that combine multiple interactions, and deliberately anti-physics prompts intended to expose whether a model can distinguish plausible from impossible behavior. The listed coverage includes object motion, energy conservation, rigid-body interactions, and human and animal motion.
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The benchmark describes two evaluation routes: human assessment and a zero-shot multimodal-language-model evaluator. Those routes are not automatically equivalent. Human judgments may be sensitive to the rubric and annotator agreement, while an automated evaluator can inherit blind spots from its training and prompting. A result should therefore state which route was used, along with the prompt set and benchmark version.
Physics-IQ Verified: a held-out test of physical understanding
Physics-IQ spans five areas: solid mechanics, fluid dynamics, optics, thermodynamics, and magnetism. Its project repository recommends the Verified version for evaluation. That breadth makes it useful when a team wants to test more than everyday collision and gravity examples.
The central operational rule is to keep the test material untouched. The project warns against using test examples for prompt optimization, best-of-N selection, or other tuning. If a team repeatedly rewrites prompts or chooses the most favorable generation after looking at held-out examples, the resulting score no longer represents an independent test. Use separate development examples for iteration and reserve the Verified material for the final run.
PhyProbe: a clip-level violation signal
PhyProbe is intended to score an individual video. Its project page says inference requires one video and does not require a prompt or reference pair. That makes it convenient for reviewing an existing generation archive, including clips whose original prompts were lost or are not suitable for standardized comparison.
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Its output is a continuous physical-violation score on an anchored scale, with a lower score indicating greater consistency according to the project description. Because it is a learned evaluator, the number reflects the evaluator’s learned behavior as well as the clip. Check whether the content, camera motion, duration, and phenomena in your application resemble its coverage before using the score for broad claims.
PDI-Bench: geometry and perspective diagnostics
PDI-Bench concentrates on three-dimensional consistency: scale-depth alignment, 3D motion consistency, and 3D structural rigidity. It is the appropriate specialist when a clip’s problem looks like a camera or shape contradiction—for example, an object changing size without a corresponding depth change, a rigid item bending, or a moving subject violating a stable spatial relationship.
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Its project page states that lower PDI is better. PDI is not a complete physical-realism grade: it does not by itself establish that forces, energy, material properties, or cause and effect are correct. Pair it with a broader physics suite when those behaviors matter.
GAUGE: measured dynamics instead of appearance alone
GAUGE is aimed at settings where physical fidelity can be checked against controlled real-world experiments. It uses measured trajectories, calibrated parameters, uncertainty estimates, and task-specific observables to compare generated or simulated dynamics with physical behavior. This is a different evidentiary standard from asking people or an automated model whether a clip looks plausible.
The Shanghai AI Laboratory repository describes 22 task families and approximately 1,560 motion-capture trials. Those resources indicate a measurement-oriented scope, but they do not make every task relevant to every application. GAUGE is most useful when your use case has comparable physical trials and observables; it is a poor fit if the only available evidence is a text prompt and a subjective impression.
VideoPhy: caption adherence plus physical commonsense
VideoPhy evaluates generated clips against real-world activities and physical laws through human judgments and also introduces an automatic evaluator. It is useful when two questions must be separated: did the model depict the requested activity, and did the activity obey plausible mechanics?
Keep those outcomes distinct in a report. A clip can follow a caption while violating a physical law, or look physically sensible while omitting an important part of the caption. VideoPhy’s published model figures belong to its own benchmark and generation setup, so they should not be treated as a permanent ranking of current systems.
How do I choose a tool for my evaluation?
Start with the failure you need to detect and the evidence you can provide. Do not pick a benchmark solely because it returns a single number.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11- For broad model screening: choose a prompt-based suite such as PhyWorldBench or Physics-IQ Verified. Broad coverage is useful when you need to probe several phenomena rather than diagnose one known defect.
- For perspective or shape problems: use PDI-Bench as a focused check on scale-depth alignment, 3D motion, and structural rigidity. Pair it with a broader test if you also need to assess other physical behavior.
- For a quick clip-level signal: PhyProbe is designed to score an individual video without a prompt or reference pair. Treat that result as a diagnostic clue, not a conclusive verdict.
- For quantitative physical fidelity: consider GAUGE when you have relevant controlled experiments and measurable observables, such as trajectories. Its value depends on whether its task protocols match the behavior you are evaluating.
- For caption-plus-physics assessment: VideoPhy is relevant when the question includes whether a clip both depicts the requested activity and follows physical laws. Keep semantic adherence separate from mechanics when reporting outcomes.
Compare candidates along five axes: the phenomena and failure types covered; whether inputs require prompts, reference clips, or only a generated clip; the scoring basis; whether the tool is intended for model-level comparison or clip-level diagnosis; and the applicable test-data and software terms. These differences are more informative than a marketing-style claim that one system is simply “more accurate.”
A layered evaluation is often more informative than selecting a winner from one benchmark: use broad coverage to discover failure patterns, then a specialized measure to inspect the type of failure that matters to your application. If no relevant real-world measurements exist, do not describe a prompt-based or learned score as a measured physical-fidelity result.
How should I design the test set?
Write the evaluation claim before writing prompts. Define which physical phenomena matter, what counts as a violation, and whether the test is intended to compare models or triage individual clips. Include both ordinary scenarios and edge cases that could reveal a known weakness. PhyWorldBench’s inclusion of anti-physics prompts illustrates the value of deliberately testing impossible as well as plausible situations.
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For model comparisons, keep generation settings consistent: use the same prompt wording, number of samples, duration, resolution, and post-processing for every model unless the protocol explicitly varies them. Record failed generations rather than silently replacing them. If best-of-N selection is part of a product workflow, report it as a separate condition; do not use it to inflate a held-out benchmark score.
For clip-level diagnostics, preserve the original file and metadata. Note camera movement, cuts, frame rate, duration, and any edits that could affect geometry or motion. A score on an unedited clip is not necessarily comparable with a score after stabilization, interpolation, or selective trimming.
How should I run an evaluation without contaminating the result?
Keep evaluation examples separate from model or prompt development. This is especially important for Physics-IQ: its repository explicitly warns against using test examples for prompt optimization, best-of-N selection, or other tuning. Repeatedly selecting or rewriting generations against held-out examples turns the test set into part of the optimization process.
- Define the claim first. Decide whether you are testing broad physical plausibility, a specific geometry issue, a clip-level violation signal, or agreement with measured real-world dynamics. Also decide whether caption adherence is a separate outcome.
- Choose the matching resource. Use the tool whose input and scoring basis fit the claim. Prompt-based comparisons, one-video scoring, geometry checks, and trajectory-based comparisons are not substitutes for one another.
- Lock the protocol. Record the prompt set, generation setup, benchmark or dataset version, evaluator version, and whether judgments come from people, a learned evaluator, geometry measures, or physical measurements. If you use PhyWorldBench, state whether you used its human-assessment route or zero-shot multimodal-language-model route.
- Preserve held-out data. Do not revise prompts or choose the best generation based on test examples. Use separate development material for iteration and reserve the test material for the reported evaluation.
- Review representative failures. Save examples for each important category, such as sliding contacts, broken object permanence, impossible trajectories, unstable perspective, or incorrect caption action. Aggregate numbers are easier to interpret when readers can inspect the underlying behavior.
- Report results by dimension. Separate physical behavior from visual quality and prompt adherence where the protocol permits. Include failure examples or categories alongside aggregate results so a reader can see what the score does and does not represent.
- Check terms before adoption. Inspect the current dataset and software licenses and data terms. The project descriptions alone do not establish that every resource is freely usable for commercial evaluation.
What does a practical evaluation workflow look like?
Begin with a small, frozen development set to confirm that files, prompts, and scoring scripts work. Then run the held-out benchmark once under the locked protocol. For a broad comparison, summarize results by phenomenon instead of only reporting one average. A model may perform well on object motion and poorly on energy conservation; that pattern is more actionable than an undifferentiated mean.
Next, select a diagnostic based on the observed risk. Use PDI-Bench when the failures appear spatial or geometric, PhyProbe when you need a fast score for many individual clips, and GAUGE when you can reproduce the relevant physical task with measured trajectories. Keep these follow-up tests separate from the broad benchmark so readers can see which evidence comes from which method.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsFor human assessment, publish the rubric and explain whether raters judged physical plausibility, caption adherence, or both. For automated routes, name the evaluator and version and describe the supplied inputs. For measured comparisons, state the calibration, observables, uncertainty treatment, and task protocol. These details determine what a result means.
What do the published figures establish—and what do they not?
The figures below offer context about particular projects and experiments. They are not directly comparable measurements of one shared quantity.
- PhyWorldBench: NVIDIA Cosmos Lab authors report 1,050 curated prompts spanning fundamental, composite, and anti-physics scenarios, and a comparison of 10 text-to-video models, including five open-source and five proprietary models. These are descriptions of that benchmark and comparison, not proof that its prompts cover every real-world use.
- Physics-IQ: The project’s framing emphasizes that “visual realism does not imply physical understanding.” A visually convincing result should therefore not be used as a proxy for correct physical behavior.
- PhyProbe: Its project page, accessed in 2026, reports 82.4 weighted-average pairwise accuracy across four physical-consistency benchmarks. That figure is an evaluator result as reported by the project; it is not itself a realism score for a new generated clip.
- GAUGE: The Shanghai AI Laboratory repository, accessed in 2026, describes 22 task families and approximately 1,560 motion-capture trials. Those resources indicate the benchmark’s measurement-oriented scope, but they do not show that every task is relevant to every application.
- VideoPhy: In its 2024 paper’s human evaluation, the best-performing model in that study, CogVideoX-5B, jointly adhered to captions and physical laws in 39.6% of instances. The number applies to that benchmark and generation setup; it should not be read as a current, universal model ranking.
As of the project descriptions and results cited here, the resources represent benchmarks, datasets, papers, and evaluators—not a single validated independent comparison of all available tools. Maintenance status, current access, and commercial-use permissions are not established uniformly. For any comparison, publish the date and versions you used, the evaluation protocol, and relevant licensing or geography constraints when those are stated by the project.
What are the operational and cost considerations?
These resources are primarily datasets, papers, code, and software evaluators rather than a central physical-testing product with a standard subscription price. No universal hardware purchase is implied. Compute requirements depend on whether you are generating videos, running a learned evaluator, processing geometry, or comparing trajectories, as well as on clip length and batch size.
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Budget for the work that the chosen protocol actually requires: generation runs, storage for source and evaluated clips, human annotation if you use a human route, and engineering time to preserve versions and held-out data. A measurement-grounded workflow can additionally require access to appropriate physical trials and calibrated measurements. Check current code, data, and commercial-use terms before committing to a deployment; access and licensing are not uniform across projects.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should I interpret a benchmark score?
Interpret it as evidence about a defined test, input, and scoring method. A human rating reflects judgments under a stated rubric; a learned evaluator reflects its learned scoring behavior; a geometry measure reflects the properties it was built to assess; and a trajectory comparison reflects measured observables for particular tasks.
For PhyProbe, the project describes a lower score as greater consistency on its anchored scale. PDI-Bench likewise says lower PDI is better. Do not compare either direction or magnitude with another tool’s score unless the projects establish a shared scale and protocol. The available descriptions do not establish such a universal cross-tool scale.
When reporting a result, name the benchmark and version, the evaluator and route, the inputs supplied, and the subset of physical phenomena covered. If a model does well on caption adherence but struggles with motion, report that difference instead of compressing both into an undifferentiated claim of realism.
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For an evaluation or product team beginning an assessment, use one broad test plus one diagnostic that matches the risk. PhyWorldBench or Physics-IQ Verified can provide broad prompt-based coverage; add PDI-Bench for geometry-focused questions or PhyProbe for clip-level violation scoring. Use GAUGE instead when the key question is whether dynamics agree with controlled, measured behavior and its task design fits.
Keep the resulting evidence in separate lanes: broad benchmark performance, focused diagnostic results, and measured physical fidelity where available. That makes it clearer whether a system is failing at prompt interpretation, physical behavior, geometry, or some combination—and avoids implying that one benchmark certifies all forms of physical realism.
Frequently Asked Questions
Can I compare scores from two different physical-realism benchmarks?
Not safely by their raw numbers alone. The benchmarks use different inputs, targets, and scoring bases, and the project descriptions do not establish a shared scale; compare results only within a stated protocol or alongside the underlying examples.
Does a good average score mean every generated clip is physically plausible?
No. An aggregate can conceal a narrow but important failure mode or a small set of implausible clips. Review examples and categories as well as the overall result, especially when a specific type of physical error would matter in your use case.
Can a benchmark result certify that a video model is safe for a particular application?
These resources evaluate selected physical or semantic behaviors; the descriptions do not establish them as safety certifications. Application-specific review still needs to account for the consequences of errors and the conditions the benchmark does not cover.
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Are the benchmark datasets automatically free to use in a commercial evaluation?
No general commercial-use permission is established across these projects. Check the current license and data terms for the exact dataset, code, and evaluator you plan to use.
Which tool should I use if I only have a finished video and no prompt?
PhyProbe is the clearest fit because its project page says inference takes one video without a prompt or reference pair. Use the resulting score as a diagnostic signal and confirm important findings with targeted review or another method.
Which benchmark is best for camera perspective and rigid objects?
PDI-Bench is the focused option because it examines scale-depth alignment, 3D motion consistency, and 3D structural rigidity. It should be paired with a broader physics test if forces, energy, or contact behavior also matter.
How do I test whether generated motion matches measured reality?
Use a measurement-grounded protocol such as GAUGE when you have controlled experiments, calibrated parameters, measured trajectories, uncertainty estimates, and task-specific observables that match the behavior under evaluation.
Should I use human judges or an automated evaluator?
Choose according to the claim and report the route. Human assessment exposes judgments under a stated rubric, while learned evaluators provide repeatable signals but can have coverage and transfer limits. PhyWorldBench describes both routes, so state which one you used.
Can I improve prompts after seeing held-out benchmark examples?
Not if you want an independent score. Physics-IQ explicitly warns against prompt optimization, best-of-N selection, and other tuning on test examples. Iterate on separate development material and reserve held-out examples for the final run.
Is VideoPhy a complete physical-realism test?
No. It addresses physical commonsense together with caption adherence under its benchmark and generation setup. Use a geometry, violation, or measurement-grounded tool as well when those dimensions are important.
The Bottom Line
Use a broad suite for coverage, a focused diagnostic for the failure mode, and measured comparisons only when real-world observables are available. Report the protocol, score direction, versions, and licensing context, and never treat one benchmark number as a universal certificate of physical realism.
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