LikePhys: Evaluating Intuitive Physics Understanding in Video Diffusion Models via Likelihood Preference
Authors: Jianhao Yuan
Fabio Pizzati
Francesco Pinto
Lars Kunze
Ivan Laptev
Paul Newman
Philip Torr
Daniele De Martini
Venue: ICLR 2026 Poster (2026)
Keywords: Video Generative Model
Video Diffusion Model
Intuitive Physics Understanding
Primary area: generative models

Abstract: Intuitive physics understanding in video diffusion models plays an essential role in building general-purpose physically plausible world simulators, yet accurately evaluating such capacity remains a challenging task due to the difficulty in disentangling physics correctness from visual appearance in generation. To the end, we introduce LikePhys, a training-free method that evaluates intuitive physics in video diffusion models by distinguishing physically valid and impossible videos using the denoising objective as an ELBO-based likelihood surrogate on a curated dataset of valid-invalid pairs. By testing on our constructed benchmark of twelve scenarios spanning over four physics domains, we show that our evaluation metric, Plausibility Preference Error (PPE), demonstrates strong alignment with human
preference, outperforming state-of-the-art evaluator baselines. We then systematically benchmark intuitive physics understanding in current video diffusion models. Our study further analyses how model design and inference settings affect intuitive physics understanding and highlights domain-specific capacity variations across physical laws. Empirical results show that, despite current models struggling with complex and chaotic dynamics, there is a clear trend of improvement in physics understanding as model capacity and inference settings scale up.

Published as a conference paper at ICLR 2026
LIKEPHYS: EVALUATING INTUITIVE PHYSICS UNDERSTANDING IN VIDEO DIFFUSION MODELS VIA LIKELIHOOD PREFERENCE
Jianhao Yuan
Fabio Pizzati!
Francesco Pinto
Lars Kunze
Ivan Laptev!
Paul Newmant
Philip Torr!
Daniele De Martinit
University of Oxford *MBZAA
University of Chicago
UWE Bristol
ABSTRACT
Intuitive physics understanding in video diffusion models plays an essential role in building general general-purpose purpose physically plausible world simulators, yet accurately evaluating such capacity remains challenging task due to the difficulty in dis- entangling physics correctness from visual appearance in generation. To the end, we introduce LikePhys, a training-free freee method that evaluates intuitive physics in video diffusion models by distinguishing physically valid and impossible videos using the denoising objective as an ELBO-based likelihood surrogate on a curated dataset of dniidinvvliid pairs. By testing on our constructed benchmark of twelve scenarios spanning over four physics domains, we show that our evaluation metric, Plausibility Preference Error (PPE), demonstrates strong alignment with human preference, outperforming state-of-the-art evaluator baselines. We then systemati- cally benchmark intuitive physics understanding in current video diffusion models. Our study further analyses how model design and inference settings affect intuitive physics understanding and highlights Jomain-speciife capacity variations across physical laws. Empirical results show that, despite current models struggling with complex and chaotic dynamics, there is clear trend of improvement in physics understanding as model capacity and inference settings scale!.
INTRODUCTION
Video diffusion models (VDMs) (Brooks et al., 2024; Google DeepMind, 2025; Polyak et al., 2024) have achieved impressive results in producing visually compelling videos, but they still often generate physically implausible outputs (Bansal et al., 2024; Motamed et al., 2025; Yuan et al., 2025). Ensuring generative models learn the underlying physics that govern the dynamics of visual data is essential not only to improve the outputs' quality (Kang et al., 2024; Li et al., 2025), but also a essential pre-requisit for them to serve as reliable world models (LeCun, 2022; Ha Schmidhuber, 2018) with applications in robotics (Yang et al., 2023) and autonomous driving (Hu et al., 2023; Zhao et al., 2025).
However, it is challenging to evaluate how visual generative models learn and internalise physics. A classic line of work on general vision models uses the violation-of cedatiinnoffxxpecttii offexpectation paradigm (Spelke, 1985; Baillargeon et al., 1985), which frames intuitive physics understanding as the ability to judge the plausibility of observed events (Riochet et al., 2018; Weihs et al., 2022), when presented with unrealistic videos obtained in simulation. However, extending this paradigm to generative models remains challenging. More recent approaches rely on Vision Language Models (VLMs) for plausibility judgments (Bansal et al.. 2024: Guo et al., 2025) and text-conditioned compliance measures (Meng et al.. 2024a). Although these methods provide valuable insights, they usually fail to disentangle physics from visual appearance (Motamed et al., 2025) or introduce subjective biases (Wu & Aji, 2023) and interpretive variance (Gu et al., 2024) through VLM judgments, ultimately struggling to provide a grounded assessment of intuitive physics understanding.
In this paper we propose an alternative evaluation method named LikePhys, that leverages the VDMs' density estimation capacity (Li et al., 2023) rather than relying solely on generated outputs, we show in Fig. 1. Inspired by the collaton-of--epeeaaaiie the assumption that the capacity of model to assign higher likelihood to physically plaussbl visual sequences is correlated to its intuitive physics understanding. Then, we use a simulator to render paired
paradigm (Riochet et al., 2018), we start from
Data and code available at project page: https:://// //yuanji anhao508 . github. 10/11 kePhya/

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Published as a conference paper at ICLR 2026
blender
Video Diffusion
Himmoun T2V
Valid physics sample
Eoggh 5-33
Athioo-essmtted probebity denety
Rintt T2V-144
447
Ranch
465
Couskiee
462
Invalid physics samples
Medis
Pwetherge
Dw SECC
Continuum mechanics
M > p|m)
> mlm)? 24 2(mm]
ush
Pausbbbl proferaice eror cakulation
LiwaPty rankhin
Figure I: Overview of LikePhys. Our intuition is that a video diffusion model with a well learned underlying physics distribution should assign higher likelihoods to valid samples that obey physies laws and lower likelihoods to those invalid samples that violate them. We use Blender to create valid and invalid sample pairs through controlled physics parameters over multiple physics domains and scenarios (left). We then compare the diffusion model likelihood estimates over the constructed dataset extract a quantitative intuitive physics understanding measure, the Plausibility Preference Error (PPE) (middle). Hence, we can compute a ranking of average PPE across pre-trained video diffusion models, that correlates with human preference. Lower values indicate stronger intuitive physics understanding (right).
videos. In one, render physically realistic phenomena, while in the other introduce controlled violation of physics. Importantly, we keep the visual appearance consistent in the pair, ensuring that any difference resulting from processing the videos can be attributed solely to the breach of physics principles. Both rendered videos are then corrupted with noise and processed by the diffusion denoising network, where noise prediction losses serve as a proxy for sample likelihood (Ho et al., 2020). We then calculate the Plausibility Preference Error (PPE), i.e. we assume positive scores if the physically plausible sample has higher likelihood, and negative otherwise. Aggregating PPE results across pairs yields single score that quantifies the intuitive physics understanding of a single model.
To enable our evaluation, we construct synthetic benchmark of twelve scenarios spanning Rigid Body Mechanics, Continuum Mechanics, Fluid Mechanics, and Optical Effects. Importantly, we design each physies scenario to be relatively simple, with clearly attributable governing physics dynamics, and consistent in appearance, that any likelihood variation due to model preferences for visual quality cancels out during pairwise preference comparisons. Using this benchmark, we rank the verifica-
performance of 12 state-of-te tht--hterttt VDMs in terms of PPE, based human preference-base tion. Furthermore, we analyse the key factors in model design and inference settings influencing their intuitive physies understanding, and examine domain specific capacity variations across the physics domain and laws to highlight the limitations of current VDMs. Empirical results demonstrate that our proposed metric robust proxy for physics understanding in VDMs, offering actionable insights into both their current limitations and their potential for progress. Our key contributions are the following:
We propose LikePhys, training-free free, likelihood evaluation method for intuitive physics in VDMs with verified alignment to human preference.
We benchmark pre-trained VDMs with a constructed dataset including twelve physics scenarios across rigid-body mechanics, continuum mechanies, fluid mechanies, and optical effects, each designed to isolate a specific physics violation under matched visual conditions.
We conduct comprehensive analysis on intuitive physics understanding of state-of-the-an VDMs, showing how architectural design and inference settings affect physics understanding, and highlighting variations in capacity across physics domains.
2 RELATED WORK
2.1 VIDEO DIFFUSION MODELS
VDMs have emerged as powerful generative frameworks for producing realistic video sequences
(Esser al., 2023; Brooks al., 2024; Zhou al., 2024; Girdhar et al., 2024). Early approaches
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L' =Bulle-eo(st
Valid Physies
V
Plausibility Preference Error M
Diffusion Model
1c <t]
MN
= - - -0(
Invalid Physics
Figure 2: Method Overview. We prepare groups of videos via physics simulations with valid samples obeying physical laws and invalid samples containing deliberate violations. We then inject Gaussian noise into these videos and diffusion model to predict the noise and compute the denoising loss. For each valid-invalid pair, we compute a likelihood preference ratio that quantifies how the model favors physically plausible sequences, serving as proxy for physics understanding.
such as VDM (Ho al., 2022), AnimateDiff (Guo al., 2024), and ModelScope (Wang al., 2023) relied on 3D UNets (Ronneberger et al., 2015) to demonstrate the feasibility of spatio-temporal modeling. More recent systems, including Cog VideoX (Yang et al., 2024), Hunyuan T2V (Kong et al., 2024), Wan (Wan et al., 2025), and LTX (HaCohen et al., 2024), adopt Transformer-bis based backbones the Diffusion Transformer (DiT) style (Peebles & Xie, 2023; Vaswani et al., 2017), advancing long-sequence generation, visual quality, and inference efficiency. Despite these advances, remains unclear whether current models capture underlying physical principles (Motamed et al., 2025). In this work, we propose general evaluation protocol to assess the extent which VDMs implicitly learn the laws of physics.
2.2 INTUITIVE PHYSICS UNDERSTANDING
Intuitive physics understanding is fundamental to model's ability to reason about and predict scenes under physics laws (Ates al., 2020; Bear al., 2021; Zhan et al., 2024; Meng al., 2024b). One important line of work builds on the violation-of--eeee onattionno--expettationnn paradigm (Spelke, 1985; Baillargeon al., 1985; Margoni et al., 2024), framing physics understanding in general vision models as the ability to judge the plausibility of observed events (Smith et al., 2019; Riochet et al., 2020; Weihs et al., 2022; Garrido et al., 2025). For example, IntPhys1/2 (Riochet et al., 2018; Bordes al., 2025) assess intuitive physics using tightly controlled synthetic video pairs that differ only by single violation of permanence, immutability, spatio-tempor principle in isolation. These works are mostly built on simulated data and have been successful in evaluating the physics understanding of various vision models (Garrido et al., 2025; Bordes et al., 2025). However, extending this protocol to visual generative models is non-trivial: it typically relies on context-conditioned generation and pixel reconstruction on target frames as plausibility proxy, and it remains unclear how adapt this setup text-conditioned video diffusion models that lack image conditioning and are not naturally discriminative.
continuity, or solidity to study each physics
2.3 PHYSICS EVALUATION IN VIDEO GENERATIVE MODELS
Some recent works focus on evaluating video generative models. A popular branch of methods (Guo al., 2025; Meng et al., 2024a) uses VLM with question-annwering templates assess adherence to physical laws in generated videos. For example. VideoPhy 1/2 (Bansal et al., 2024; 2025) collect human-annotated synthetic videos and finetune VLM judges to align with human preferences on physical plausibility. However, these approaches are subject to visual appearance biases across different generative models (Wu & Aji. 2023) and interpretive variance across prompts and judging templates (Gu et al., 2024). Physics-IQ (Motamed et al., 2025) and Morpheus (Zhang et al., 2025) address these issues by comparing generated videos with paired real recordings via pixel/object-mat analyses and by fitting dynamical models to extract physical quantities, then scoring adherence to physical conservation laws, respectively. However, they rely on image-conditioned generation, and its extension text-to-video generation also remains unclear.
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Published as a conference paper at ICLR 2026
Valid Physics
Invalid Physics
Rigid- Body
Mechanics
Claes Drape
Cash
Hoonet Fla
Creck
Droplee
Moving Saatio
Fluid Mechanics
Optical Effects
Figure 3: Evaluation benchmark. We organise 12 scenarios derived from four physical domains (Rigid Body Mechanics, Fluid Mechanics, Continuum Mechanies, Optical Effects) with their relative proportions (left). Rows from top to bottom (middle and right) show examples from four optical phenomena, rigid-body mechanics, continuum mechanics, and fluid mechanics. We display valid simulation (middle) and corresponding invalid variants (right) for physics violation in each domain.
To move beyond these limitations and provide alternative evaluation from likelihood preference perspective, we take inspiration from the violation-ol cattiii-of--pppccaatton paradigm while removing the need for conditional generation or pixel-level alignment. Our method adapts the density estimation capability (Li et al., 2023) of VDMs and performs pairwise comparisons by directly evaluating sample likelihoods through the denoising loss proxy. This design keeps the metric model-agnostic and appearance-agnostee avoiding confounds from visual artifacts while focusing directly on physics understanding.
METHODOLOGY
We aim to evaluate the intuitive physics understanding of diffusion-based video generative models. We first define physics understanding from distributional perspective, starting from preliminary assumptions (Section 3.1) and then deriving a likelihood preference score that quantifies the model's ability assign higher probability physically valid samples than invalid (Section 3.2). For the evaluation of LikePhys, we generate a comprehensive dataset controlled video sequences using simulations (Section 3.3), which we use to test the sensitivity of the model physical plausibility.
3.1 PRELIMINARIES ON VIDEO DIFFUSION MODELS
Diffusion Probabilistic Models (Sohl-Dickstein al., 2015; Ho et al., 2020; Nichol & Dhariwal, 2021) learn the data distribution by inverting forward Markov noising process: g(0, xt-1)
M(r; /1 - 14-1; 9,1), 3e E (0, 1), A parameterised model then learns the reverse process Pe p m4) = (z Me (xt, !), of I by minimising a noise prediction loss which serves as an ELBO-based surrogate for the negative logelikeli The noise prediction loss is calculated by reconstructing the ground truth noise injected on I, with a denoising network eg, following:
Ldenoise (0: v.) = E,,, ||c (z,, |] E.z |- log poga)( + const.
VDMs built the framework that models dynamics in sequence of frames FxC The denoising network is therefore designed to capture both spatial structure within each frame and temporal relationship across frames, allowing the model to implicitly learn motion dynamics in addition to appearance.
3.2 PHYSICS UNDERSTANDING AS LIKELIIOOD PREFERENCE
Our intuition is to formalise intuitive physics understanding in a distributional perspective. Let Pphys (x) denote the distribution over videos that strictly obey physical laws, where video {o X (p) 0}. By definition, any valid
sequence. We define its support Mphys
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Published as a conference paper at ICLR 2026
sample x* E Mphys satisfies real-world physical laws, whereas any invalid sample x: & Mphys violates some of them. We consider a diffusion model pe that learn has a perfect intuitive physics understanding if, for every valid-invalio pair (x+,,²")
pe (2*)) > p=
(2)
Hence, if diffusion model has learned the distribution correctly, it should assign a higher likelihood to valid samples when they are paired with visually matched invalid samples. As shown in Eq. (1), the denoising loss is an ELBO--based likelihood surrogate. Thus, lower denoising loss corresponds to higher likelihood under pa. For any valid-invalid pair (zz ,x") we therefore have
Po (x > Pa( - Lenoisee (0:x*) < Ldeeoise (8;
(3)
To evaluate this likelihood- prrffeeoddeeefeeeceee protocol in practice, first identify 12 physics scenarios. For each scenario, we create R. controlled variations to account for variability in both physics parameters and confounding visual factors (with R 10 in our benchmark; see Sec. 3.3). For each variation (1,..., R), we collect sets of controlled video pairs following the violation-of-exxeecatton paradigm (Margoni al., 2024; Bordes al., 2025):
V = { MM
V, = J.V
where V and V- denote valid and invalid samples for variation r, respectively. For all video samples in ViJ and V,, we ensure they are simple and consistent in visual appearance, with the only differences arising from violations of the governing physics dynamics. This ensures that any likelihood variation due to model preferences on visual style cancels out, enabling an unbiased comparison across different video diffusion models.
We then perturb both videos in each pair with the same Gaussian noise at sampled diffusion timesteps, pass them through the denoising network to predict the noise, and average the loss over timesteps as in Fig. 2, obtaining a denoising loss deeooise (0; x) for each video. For a given variation r, we say the model has violated expectation on the pair (#,.) F if it assigns no greater likelihood (i.c., no lower denoising loss) to the valid sample than to the invalid one. Aggregating over x pairs within each variation and then averaging across variations, we define the Plausibility Preference Error (PPE):
Mr N. M
PPE
1
1
L
Edenoise (0: 2 Edenoise (0; x.
(5)
R
M, N,
j=1
where 1[:] is the indicator function and the inner average corresponds to the rate that model mis- assign higher likelihood invalid video sample for each variation r. A lower PPE indicates that po consistently prefers valid samples across variations, thus measuring the model's likelihood preference for physically plausible videos and serving as proxy for learned intuitive physics.
3.3 EVALUATION BENCHMARK CONSTRUCTION
To rigorously evaluate the learned physics, we require controlled video pairs that differ only in physical validity. In practice, it is infeasible to obtain such matched pairs from real-world data, where physies laws cannot be systematically violated. We therefore construct a synthetic simulation benchmark spanning 12 physical scenarios across 4 domains, which allows precise control over physics parameters. All videos are rendered in Blender (Community, 2018) at 512 x 512 resolution with 60 frames. Each scenario is generated with R 10 variations that vary both physics parameters and visual factors (e.g., object shape, texture, or environment). Within each variation, we generate M valid videos that strictly obey the relevant law, and N invalid variants that introduce a single controlled violation (e.g., superelastic bounces or ghosted shadows). By holding camera angle, lighting, textures, and object geometry constant within a single variation, the valid and invalid vidcos differ only in physical plausibility, ensuring that any measured likelihood gap can be attributed to physics violations. As shown in Fig. 3, the benchmark composition is as follows (see Apx. B for more details):
Rigid Body Mechanies: five cases including Ball Collision, Ball Drop, Block Slide, Pendulum Oscillation, and Pyramid Impact. They cover collision dynamics, periodic motion, gravity, and energy transfer. Valid examples conserve momentum and energy, follow free-fall under gravity, and
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Published as a conference paper at ICLR 2026
Table Model ranking. Plausibility Preference Error (%) across twelve controlled physies scenarios for various video diffusson models. Lower values indicate stronger physics understanding. Models are ordered by increasing average performance (Avg) across all scenarios. Best and second-best per scenario are bold and underlined.
Rigid Body Mechanics
Cont. Mechanics
Fluid Mechanics
Optical Efffects
Ball Collision
Ball
Block Slide
Pendulum Oscillation
Pyramid Impact
Cloth Drape
Cloch Waving
Faucet
Droplet
River
Orbit
Moving
Drop
Flow
Fall
Flow
Shadow
Shadow
Drim
63.3
60.0
65.0
51.7
61.1
52.9
78.6
62.0
63.3
44.0
71.7
56,0
60.8
Diff SDXL
63.3
66.7
38.3
70.0
68.9
47.1
82.9
54,0
60.0
46.0
43.3
32.0
56.0
ZeroScope
53.3
55.0
46.7
58.3
73.3
38.6
84.3
46.0
61.7
40.0
40.0
42.0
53.3
ModelScope
51.7
53.3
46.7
66.7
78.9
40.0
84.3
40.0
61.7
40.0
33.3
38.0
52.9
Mochi
40,0
50.0
31.7
65.0
84.4
28.6
82.9
38.0
60.0
44.0
58.3
40.0
51.9
Cog Videix SB
43.3
41.7
61.7
65.0
83.3
38.6
81.4
36.0
53.3
40.0
23.3
30,0
49.8
CogVidenX 2B
40.0
38.3
60.0
63.3
83.3
36.7
81.4
36.0
50.0
40.0
18.3
32.0
48.2
1km2.11T2V..1
43.3
53.3
66.7
20.0
40.0
65.7
32.9
60.0
33.3
78.0
23.3
60.0
48.0
LTX v0.9.5
36.7
58.3
35.0
68.3
18.9
47.1
48.6
32.0
28.3
40.0
75.0
48.0
44.7
CogVideoXI
61.7
50.0
51.7
31.7
25.6
52.9
22.9
54.0
21.7
68.0
23.3
62.0
43.8
1-2211-B
41.7
56.7
61.7
13.3
34.3
56.0
15.0
16.7
60.0
43.8
Hunyuan T2V
33.3
51.7
25.0
21.7
38.0
40.0
38.3
44.0
maintain shape and continuity. Invalid variants break these rules through anomalies such as excessive restitution, teleportation, or implausible energy transfer. Continuum Mechanics: two cases including Cloth Drape and Cloth Waving. They capture material deformation under gravity and aerodynamic forces. Valid examples show natural folds and consistent surface behavior. Invalid variants disrupt realism through penetrations, distortions, scrambled temporal sequences.
Fluid Mechanies: three cases including Droplet Fall, Faucet Flow, and River Flow. They address conservation of mass, viscosity, and surface tension. Valid examples generate continuous and plausible fluid patterns. Invalid variants introduce reversed flows, discontinuities, spontaneous mass changes, or temporal glitches.
Optical Effects: two cases including Moving Shadow and Orbit Shadow. They capture liget- interactions. Valid examples show smooth and consistent shadow behaviour. Invalid variants break physical plausibility by inverting, erasing, or misaligning shadows or by adding temporal inconsistencies.
EXPERIMENTS
We evaluate the intuitive physics understanding of pre-trained VDMs using LikePhys and perform systematic analysis to answer the following key questions: (1) Which models exhibit the strongest intuitive physics as measured by PPE? (2) How well does PPE align with human preference in physics correctness of generation? (3) To what extent does PPE disentangle visual appearance from intuitive physics? (4) What model design and inference factors influence models' intuitive physics? (5) Across which physics domains and laws do current models fall Short?Setting:: We benchmark pre-trained text-to-video diffusion models including AnimateDiff-SDD 5/SDXLL (Guo et al., 2024), ModelSccope/ZeroScope (Wang et al., 2023), Mochi (Team, 2024), Cog VideoX-2B/SB (Yang et al., 2024), 1- (Wan et al., 2025), Hunyuan T2V (Kong et al., 2024) and LTX (HaCohen al., 2024). To maximise performance, we adopt each model's 's recommended inference settings from its official repository and resample the frame rate and spatial resolution of generated video samples to match. We then perform zero-shot evaluation using the proposed LikePhys protocol, measuring performance via the PPE metric defined in Eq. (5) and ranking models. For each physics scenario, we report the average error over all valid-invalid pairs. For the likelihood estimation, we use the same DDIM (Song al., 2020) scheduler with 10 timesteps uniformly sampled through the noise scale. We use a fixed prompt template for each physics scenario. More details of the evaluation implementation (Apx. A), evaluation dataset construction and specification (Apx. B), inference settings (Apx. C), experiment settings (Apx. D), further applicability to other intuitive physics benchmarks (Apx. E) and visual samples (Apx. F) provided in the appendix.

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Published as a conference paper at ICLR 2026
Table 2:
Correlation to Human Preference. Kendall's correlation between Plausibility Preference Error and state-of-the-an art physics evaluators to human annotation across physics scenarios.
Ball
Ball
Block Slide
Pendulum Oscillation
Pyramid Impact
Cloth Drape
Cloth Waving
Faucet Flow
Droplet Fall
River
Orbit Shadow
Moving Shadow
Overall
Collision
Drop
Flow
VideoPhy
20.0
-11.0
26.7
-13.8
-20.9
57.6
15.3
26.3
-6.4
40.8
47.5
-9.6
38.9
VideoPhy2
-19.4
-32.2
-688
-15.9
-311
51.0
10.5
20.00
-41.0
6.8
70.4
-33.9
88.5
Qwen2.5 VL
39.4
59.0
-25.4
-19.4
14.2
49.5
-15.2
29.1
-22.2
26.9
45.2
-33.9
33.3
Like
34.4
42.6
24.3
15.0
55.9
15.4
0.0
51.0
-8.7
16.8
36.4
12.1
44.4
4.1
MAIN RESULTS
Model ranking In Tab. 1, we benchmark twelve VDMs and order them by increasing PPE. The results reveal large performance gaps across architectures. Early UNet-based models (Ronneberger et al., 2015) such as AnimateDiff and ZeroScope often record error rates above 50%, reflecting limited ability distinguish physically valid videos from invalid ones. In contrast, more recent DiT-based designs (Peebles & Xie, 2023), including the top three performers Hunyuan T2V (43.6%), Wan2.1-T2V 14B (43.8%), and CogVideo.) 1.gViieeX1.555555 (43.8%), achieve substantially lower errors across multiple scenarios (see Sec. 4.2 for further discussion).
Moreover, while there is a clear trend of improvement in recent architectures, only a handful of models significantly achieve PPE lower than the 50% random-guess threshold in more than a few scenarios, potentially due to simplicity biases, where physically implausible interactions appear visually easier to model than valid ones (Shah et al., 2020; Geirhos et al., 2020). These results highlight that while modern VDMs start to internalise physical principles, their performance varies substantially across different physics domains (see Sec. 4.3 for further analysis), indicating significant room for progress in physically accurate training of video generators.
Alignment to Human Preference Moreover, we conduct human study to assess how well PPE serves as a proxy and aligns with human judgments of physics plausibility in downstream video generation. Specifically, we perform text-to- video generation with model tested in Sec. 4.1, generating 20 samples per model using the prompts that describe the designed physics scenarios in Sec. 3.3. Following the VideoPhy annotation protocol (Bansal et al., 2025), human annotators rate each video on a 1-5 scale, where indicates a severe violation of physics and indicates strong consistency (see Apx. D.2 for details). We then derive ranking of models from the aggregated human scores and compare it against the PPE-based ranking of LikePhys, measuring their agreement with Kendall's r. We note that the VLM-based rankings leverage the dataset of videos generated by the candidate models, whereas the PPE-based ranking is computed solely on our synthetic benchmark and does not use any downstream model-generatee videos.
We further compare PPE against state-of-the-art automatic evaluators. including human- alignnd VLM-based metries such as VideoPhy (Bansal al., 2024) and VidcoPhy2 (Bansal et al., 2025), as well as Qwen 2.5 VL. IB-Instruct (Bai et al., 2025; Jang et al., 2025) as general VLM prompted to evaluate physical correctness. We find that PPE achieves a stronger overall correlation with human annotations, demonstrating the competitiveness of our zero-shot, training-free approach. In particular, the resulting correlation of 0.44 indicates that models with lower PPE also tend to be judged by humans as more physically consistent.
Disentanglement of Visual Appearance While we verify the correlation between PPE and physics consistency of generated videos. we examine whether it also correlates with established visual quality metrics from VBench (Huang et al., 2024) reported in Tab. 3. By doing so, we aim to determine whether perceptual metrics already capture physical plausibility or represent an independent dimension of evaluation. As shown in the table, aesthetic quality has no significant correlation (r = -0.05) to PPE,
Table 3: Pearson correlation hetween Plausibility Pref- erence Error (PPE) and visual quality metries.
Con PPE
Aesthetic Quality
-0.05
Subject Consistency
-0.01
Background Consistency
-0.01
Motion Smoothness
0.15
Temporal Flickering
0.12
indicating metric targets physical correctness rather than aesthetic appeal. Subject consistency
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AnimateDiff
Effect Model Size
Effect of Number of Frames
Avg. PPE
0.6
WenZ 1-124-1.--10
AnimateDiff SOXL
Hode Scope
ZeroScope
0.5-
0.5
Cegildeox
ITX
Co
Mungue T2V
0.4
20
60
Model
No. Frames
Effect Data Scale
Effect of CFG Strength
ArlimteDiff
0.7
Avg. PPE
0.6
Wanz 1-724-1.00 3D
0.4
0.5
CogVileaX- $5
0.3
Cogghdeex
0.2
10M
20M
35M
120M
Training Data Size (M)
Strength
AnimataDiff
CogVidocX
Hunyuan
ModelScope
ZaroScopa
AnimateDiff SDXI
CogVidessXS58
ITX
W
Figure 4: Analysis on inftuencing factors. Effect of model size, showing a steady overall decline in PPE (top left). Effect of training data size, where larger corpora generally yield lower error, though the correlation is weaker than that of model size (bottom left). Effect of context window size, showing consistent improvement in physics understanding as the window increases (top right). Effect of classifier-free guidance (CFG) strength, indicating that physics understanding remains largely stable across different strengths (bottom right).
(r = -0011) and background consistency (r = -.001) show very weak correlations, consistent with the fact that the feature similarity- based metric does not fully capture physics correctness of dynamic motion. In contrast, motion smoothness (r = 0.15) and temporal flickering (r = 0.12) have moderate positive correlations with PPE. This is expected, as smooth, flicker-free motion can be a partial indicator of physical plausibility. Overall, these results confirm that our method evaluates aspects of physical reasoning that are largely orthogonal visual quality measures, providing complementary insights into video generative models' capabilities and failure modes.
4.2 ANALYSIS ON INFLUENCING FACTORS
Architecture and Data. We analyse the physics understanding with respect to the scaling of model parameters and training data by plotting the average PPE in correlation to model parameters and training samples. As shown in Fig. 4. larger models consistently outperforms smaller variants. Largest models are almost exclusively DiT-based, showing the scalability of these architectures in capturing complex spatiotemporal dependencies. Overall, the average PPE also decreases with a larger training dataset size, indicating the effectiveness of architecture advancement and scaling to improve intuitive physics understanding. Please note that Hunyuan T2V does not disclose the training dataset cardinality.
Role of Number of Frames. Physical dynamics unfold over time, and the number of output frames is a critical design parameter for video diffusion model's 's physics understanding. As shown in Fig. 4, most models exhibit steady decrease in PPE the number of frames increases. This trend suggests that providing longer temporal context allows the model to capture more complex interactions and improves its implicit physical reasoning.
Role of Classifier-free Guidance. Classifier-free guidance (CFG) (Ho & Salimans, 2022) is widely used to balance fidelity and diversity in diffusion sampling, but its effect on physics understanding remains unclear. We evaluate models across varying CFG strengths and find that guidance scale has only a marginal impact on physics understanding. This insensitivity suggests that, unlike visual quality metrics, physics plausibility is governed primarily by the learned model distribution, while inference- cerrencee-imeeeeee calibration of noise prediction plays only a minor role. Consequently, CFG can be tuned for visual quality without compromising the model's physics understanding.
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Physics Domains
Physics Laws
Aveerage Plausibiiity ireference Entr (N))
03
23-
13
1a1 Body
Fluid
Oetcal
Tanpora
Sputsl
Conagreat on Corsevitton
Seamabre
Optical
Nateral
Figure 5: Analysis across physics domains and laws. On the left, we report detailed PPE across the four physics domains. Fluid Mechanics cases exhibit both the highest average error, while Rigid Body and Continuum Mechanics scenarios show moderate errors; Optical Effects cases lie in between. On the right, we map our domains to seven physics laws for a fine- fine-graine anee-graied analysis. Temporal continuity and conservation of energy show wide variation and higher median errors, whereas geometric invariance and optical consistency are handled more reliably.
4.3 ANALYSIS ACROSS PHYSICS DOMAINS AND LAWS
We investigate VDMs' physics understanding across different domains and laws presented in Sec. 3.3. As shown in Fig. 5, we report the average PPE across all models for each of the four proposed domains. Rigid Body and Continuum Mcchanics show moderate errors with tight agreement across models, while Fluid Mechanics exhibits both higher median error and greater variability, where faucet flow or droplet fall scenes yield 30 to 40% PPE, but complex river flows often exceed 70%. Optical Effects incur the lowest errors, likely because large-scale image and video corpora strongly constrain photometric and geometric regularities. These findings indicate challenges in modelling nonlinear, multiscale dynamics.
We further analyse performance by physics law. Following the mapping in Apx. D.1, we group common invalid failure modes into seven laws and report the average PPE for each law. As shown in Fig. 5, temporal continuity shows the largest variance across models, indicating unstable long-range coherence when motion spans many frames or occlusions occur. Spatial continuity and conservation principles such momentum and mass also yield high errors, which is consistent with the absence of global constraints in standard training objectives and samplers. contrast, geometric invariance and optical consistency are better satisfied, likely because they align with priors learned from abundant static imagery and short clips. Material response remains moderately challenging, reflecting difficulty contact events, frictional interactions, and surface compliance that require accurate high frequency detail. Overall, models capture several structural and photometric regularities but still struggle with laws that require global coupling through time and space, suggesting future work longer context training, multiscale memory, and physics-aware objectives that promote conservation and continuity.
4.4 ABLATION ON EVALUATION PROTOCOL DESIGN
Beyond analysing model capacity, we also assess the robustness of our evaluation protocol by examining two key design factors that influence implicit physics understanding.
Timestep Selection. To accurately approximate the likelihood, we inject Gaussian noise at multiple timesteps. To adopt a proper sampling strategy, we investigate the denoising loss difference between valid and invalid sample pairs for four representative scenarios across the entire noise schedule. We found that (1) the overall preference trend consistent across most timesteps for each model, and (2) the timesteps at which valid-invall separation peaks vary by model and scenario. These observations imply that oversampling any single region could bias our estimate; therefore, we adopt uniform sampling of ten timesteps per paired comparison, striking balance between computational efficiency and stable estimation.
Prompt Robustness. For text-to-video diffusion models, also evaluate sensitivity to the choice of text prompt. For the same four scenarios, we create eight representative prompt variants. We then report the mean plausibility-preeeree error across prompts, with the standard deviation capturing

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Fig Body Mocionic
CorOhuum Mecharkss
Rigid Body Mecnanics
Contruun Mechanics
PRAC Phod Erre
Opticll
Faud
Aénosteeeerylle TIN
Optical Phetomens
Aooupe Nooo Intoorto Bro
Figure 6: Ablation on Evaluation Protocol Design Factors. Left: Denoising loss differences be- tween valid and invalid sample pairs across 1000 uniformly sampled timesteps for four representative physics scenarios. Right: PPE across multiple text-prompt variants, demonstrating robustness to prompt choice.
sensitivity to prompt choice. As shown in Fig. 6, we observe no significant change in discriminative performance when varying the prompt. This indicates that using the likelihood estimation from the text-conditioned distribution under different reasonable prompts would give similar performance. This robustness arises from controlled data-generation process, which isolates only the physical-law compliance variable as the only error source.
5 DISCUSSION AND CONCLUSION
In this paper, we introduce LikePhys, a method for evaluating physical understanding in video diffusion models. By leveraging the violation-of-expeettaioo paradigm together with the density estimation capability of video diffusion models, we ensure that any difference in denoising loss within controlled video pair arises purely from physical implausibility. While our studies show the effectiveness of LikePhys in evaluating physics understanding, our approach also has limitations. We further discuss them and open new doors for research.
Empirical Assessment of Physics Understanding. Our method assesses whether the distribution learned by a video generative model is close to a physics-plausible distribution by comparing the likelihoods the model assigns physically valid and invalid video pairs. Several factors can influence this physics understanding capacity, such as the training data distribution, suboptimal optimisation, and the training objective. However, do not make any prior assumption about the cause of this capacity. Instead, LikePhys provides an empirical assessment of the resulting physics understanding.
Cost of data curation. One limitation of our method is that it relies controlled physics violations, which is impossible to obtain in real-world. Extending to a broader range of scenarios would require additional data curation in simulator video editing. While this is more costly compared to text-conditioned generation-bass evaluation protocols, our approach offers the unique advantage of precisely controlling valid-invalid pairs to probe specific laws and failure patterns.
Accessing Noise Prediction Our method requires access to the noise prediction error of the model, which makes it difficult evaluate closed-source models (Google DeepMind, 2025; Brooks et al., 2024). However, this requirement is less restrictive in the open source community. We believe that, during the development of dlseddsouuree models, our proposed method can be used as indicator for monitoring training progress and selecting checkpoints during model release, which may require ranking hundreds of models.
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ACKNOWLEDGMENTS
This work was supported by the EPSRC Programme Grant "From Sensing to Collaboration" (EP/V000748/1) and RAi UK Enterprise Fellowship supported by EPSRC (EP/Y009800/1).
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LLM USAGE
We use LLM for grammar polishing in paper writing, No text, code, or results were accepted without author verification. All technical content, claims, and conclusions are the authors' own.
A DETAILS ON EVALUATION PROTOCOL
provide more details on implementation of our ereefieood-preference evaluation protocol.
Algorithm Likelihood Preference Based Physics Understanding Evaluation
Require:
Physics scenario subgroups, each with: V = set of M physically valid videos V = set of M physically invalid videos; Pretrained diffusion model pe; Number of diffusion timesteps maxx Number of noise samples per timestep N.;
Ensure: Plausibility Preference Error PPE
1: Initialize error count <-
2: Initialize total comparisons T'
3: for each physics subgroup do
4: Step Compute denoising losses for all videos V UV~ do
5: for each video
6: Ldemoise (x) 6-0 0
7:
for / to max do
8:
for n to N. do Sample noise 10 Compute noisy frame
9:
10:
31 VoLx + v1-
11:
Predict < c (2t, t) Accumulate
12:
Ldenoise t Ldeuoise (x) + |le - 212
end for end for
14:
15:
Normalize:
Ldencise (x)
Ldenoise (2)
Timx x Ne
16:
end for
17:
Step 2: Pairwise comparisons between valid and invalid videos
18:
for each x+ E V do
19:
for each x E V do
20:
T T+
21:
Leteooise (x+) > Laenoise then
22:
E- E + model prefers invalid over valid)
23:
end
24:
end for
25:
end for
end for
27:
Compute error rate:
E PPE
T
28: return PPE
B DETAILS ON BENCHMARK DATASET GENERATION
We provide details on each the constructed physics scenarios, including the settings and the implementation of how the invalid cases are generated.
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Two spheres roll toward each other and collide elastically on flat surface, conserving momentum and energy. In invalid variants, restitution is altered (e.g. perfectly inelastic "sticking" super-elastic energy amplification); one sphere penetrates the other at impact; phantom forces are applied during collision; sphere radii change mid-impact; one sphere teleports through the other; collision timing is serambled (temporal disorder).
Ball Collision:
Ball Drop: A sphere drops from a fixed height onto rigid floor and bounces back under gravitational energy conservation. In invalid variants, the sphere's color changes mid-fligt;; its radius is dynamically rescaled during free fall; it bounces higher than gravity permits ("over-bounce") ); it penetrates the floor; it teleports to a different height; or its temporal sequence is disrupted (temporal disorder).
Block Slide: A rectangular block slides down inclined plane under friction and gravity, following Newton's 's laws. In invalid variants, the block hovers above the plane; it moves erratically, breaking Newtonian motion; spurious jitter is injected; its dimensions change as it slides; it teleports partway down the slope; or its time evolution is scrambled (temporal disorder).
Cloth Drape: A rectangular cloth drapes over cylindrical support, responding to gravity and bending forces while preserving surface continuity. In invalid variants, the cloth's color changes mid-siiulation; it penetrates the cylinder or ground; it folds in impossible ways, violating material continuity; it behaves like a rigid sheet with no natural flutter; or its temporal evolution is scrambled (temporal disorder).
Cloth Waving: piece of cloth attached a vertical pole flutters under constant wind force, exhibiting realistic wave propagation and strain. In invalid variants, sections freeze instantly; fragments shatter mid-wave; parts teleport from one side to the other; an impossible 180 twist is imposed; it explodes outward as if under internal pressure; or its motion breaks into non-physical jump cuts (temporal disorder).
Pyramid Impact: A cube drops onto a pyramid of stacked spheres, transferring energy and knocking spheres off under gravity. In invalid variants, collision energy is artificially violation); holes appear in the pyramid (mass continuity violation); spheres or the cube teleport through each other (spatial discontinuity); gravity is negated so objects float unpredictably.
amplified or damped
Pendulum Oscillation:
pendulum bob swings on a rigid rod under constant gravity, following consistent periodic arc. In invalid variants, the rod breaks mid-swing (path discontinuity); the bob disappears for segments; trajectory deviates from the cireular path; time intermittently freezes (temporal disorder); or the length/frequency varies randomly (frequency variation).
Droplet Fall: A single droplet falls under gravity, forming coherent drop and splash while conserving mass and momentum. invalid variants, antigravity forees make the droplet rise; the stream fragments into disconnected blobs; fluid mass is spontaneously created or removed conss-oonsevationn violation); viscosity becomes negative or oscillates; particles self-attract unnaturally; its temporal sequence is scrambled (temporal disorder).
Faucet Flow: faucet pours water into a transparent tank, forming continuous stream that collects and splashes, conserving mass and momentum. In invalid variants, fluid color shifts mid-flow; the stream fractures into non-coalescing droplets; viscosity becomes negative or oscillates; fluid mass is injected removed arbitrarily (mass-conservation violation); phase shifts alternate liquid-gas instantly; particles self-attract unnaturally; droplets teleport across the tank; temporal order is scrambled (temporal disorder).
River Flow: Water flows steadily downstream along a riverbed, exhibiting realistic laminar turbulent patterns under gravity. In invalid variants, the flow fractures into isolated droplets; an invisible barrier blocks water without visual cues; fluid mass vanishes mid-flow; phases shift liquid -ooiddd-------iiio =iquid- timestamps jump (temporal disorder).
Moving Shadow: A solid object moves across a ground plane under fixed illumination, producing a consistent, smoothly moving shadow. In invalid variants, the shadow inverts onto the ceiling, it vanishes entirely; it appears without its caster; its shape mismatches the object; it teleports away; or its temporal sequence is scrambled (temporal disorder).
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Orbit Shadow: A shadow orbits around object under fixed lighting, tracing smooth circular path. In invalid variants, the shadow inverts direction or plane; vanishes mid-orbit; it detaches from its caster; its geometry distorts implausibly; it teleports along its path; its temporal order is scrambled (temporal disorder).
C ADDITIONAL DETAILS OF INFERENCE SETTINGS
Our evaluation leverages off-the-shell video diffusion pipelines in zero-shot setting. For each model, we adopt the officially recommended spatial and temporal resolutions as shown in Tab. 4 and apply uniform noise scheduler (Euler discrete) over a fixed number of timesteps (T' 10) uniform sampling found optimal in Diffusion Classifier (Li et al., 2023).
Each input clip is first uniformly sampled the prescribed frame count and resized the model's native H W) resolution. The resulting sequence is encoded into a latent representation by the model's s VAE. To approximate the model's internal likelihood, we inject Gaussian noise at T evenly spaced timesteps, run the noised latent through the diffusion backbone (with classifier-free guidance the specified scale), and record mean-squared error between the true noise and the network's prediction. Averaging these per-step emros yields scalar loss that serves as proxy for log Po()).
For each physies scenario, valid and invalid variants are processed identically, and their losses are compared pairwise. The Plausibility Preference Error is computed as the fraction of valid-invalid pairs in which the invalid sample attains a lower denoising loss than the valid one. All sources of randomness (Python, NumPy, PyTorch, CUDA/cuDNN) are fixed for the video-pairs but different across subgroups via a global seed to ensure full reproducibility.
Table 4: Spatial and temporal settings for zero-shot inference.
Model
Height
Width
FPS
#Frames
AnimateDiff
512
512
16
16
AnimateDiff SDXL
1024
1024
16
16
Videed 2/SB Zeroscope ModelScope
480
720
16
49
320
576
16
24
Wan2.1 (L.3B/14B)
480
832
16
33
Hunyuan (12V / T2V)
320
512
16
61
LTX variants
480
704
25
161
All the model evaluated are text-conditioned video diffusion models, we use prompt tem- plate for each scenario that describes the physically valid event, and a shared negative prompt to discourage spurious artifacts: "worst quality, inconsistent motion, blurry, jittery, distorted".
Ball Collision: "two balls colliding with each other"
Ball Drop: "ball dropping and colliding with the ground, in empty background"
Block Slide: "a block sliding on a slope"
Pendulum Oscillation: "a pendulum swinging"
Pyramid Impact: "a cube crash into pile of spheres"
Cloth Drape: "a piece of cloth dropping to the obstacle the ground"
Cloth Waving: "a piece of cloth waving in the wind"
Droplet Fall: "a droplet falling"
Faucet Flow: "fluid flowing from faucet"
River Flow: "fluid flowing in tank with obstacles"
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Published as a conference paper at ICLR 2026
Moving Shadow: its shadow"
"light source moving around object showing
Orbit Shadow: "camera moving around an object"
D ADDITIONAL EXPERIMENTAL SETTING DETAILS
D.I
CLASSIFICATION OF PHYSICS LAWS
We provide details on how classify and summarise specific physics laws in Tab. 5.
D.2 HUMAN STUDY EXPERIMENT DETAILS
For the human study experiment, we generate 10 samples per physics scenario per model using the same prompts as shown in Apx. with prompt enhancement from GPT5. Human annotators are asked to rate each video on a scale of 1-5, following the annotation protocol of VideoPhy2 (Bansal al., 2025), where higher scores indicate stronger physical consistency.
For baseline physics evaluator comparison, we leverage VideoPhyl (Bansal et al., 2024), which rates videos on Physics Consistency (PC) and Semantic Adherence (SA) using 0-1 scale. Following the official evaluation protocol (Bansal et al., 2024), obtain per-model scores by filtering out samples with PC < 0.5 or SA < 0.5, and then computing the proportion of samples that pass this joint criterion. For VideoPhy2, which rates PC and SA 1-5 scale, we follow the official evaluation practice (Bansal et al., 2025) by filtering out samples with Physics Consistency I 4 or Semantic Adherence < 4, and then caleulating the proportion of valid samples after filtering. For Qwen2.5-VL. (Bai et al., 2025), we follow the evaluation protocol of DreamGen (Jang et al., 2025). Each video is rated 0-1 scale using the following prompt:
Does the video show good physics dynamics and showcase a good alignment with the physical world? Please be a strict judge. If it breaks the laws of physics, please answer 0. Answer for No or for Yes. Reply only 0 or 1.
D.3 INFLUENCING FACTOR EXPERIMENT DETAILS
We conduct two ablation experiments on protocol design factors: context-window length and classifier- free guidance strength. We select five representative scenarios (Ball Drop, Block Slide, Fluid, Cloth and Shadow) from our taxonomy, and five exemplar models: AnimateDiff, ModelScope, Cog VideoX, Hunyuan T2V and LTX. For number of frame experiment, we vary the number of input frames in {8, 16, 24, 32, 40, 48, 56, 64). AnimateDiff supports up to 32 frames, so we limit its trials accordingly. For CFG experiment, evaluate multiple classifier-fre free guidance scales from 1.0 to 8.0 to measure how guidance scale affects the model's ' ability to discriminate valid from invalid physics cases.
E APPLICABILITY TO OTHER INTUITIVE PHYSICS BENCHMARKS
We further demonstrate that LikePhys applies beyond our main setups by evaluating on the IntPhys Development split (Riochet al., 2018). The Dev split contains three blocks including Ol (object permanence), 02 (shape constancy), and O3 (spatio-temporal continuity). Each constructed as matched pairs of possible against impossible events. We run LikePhys unchanged and report PPE block and averaged across blocks. As shown in Tab. 6, models exhibit distinct profiles across principles (c.g., some perform better on continuity in O3 than on permanence O1), mirroring the Nomain-speciffe trends observed portability of LikePhyy and its ability to surface principle-Ievel strengths and weaknesses without task-specific tuning.
rigid-body and optics evaluations. This supports the
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Table Classification Criterion for Each Physics Law
Law Principle
Scenario
Variation key
Temporal Continrity
Ball Collision
temporal disorder
Ball Drop
temporal disorder
Block Slide
temporal disorder
Cloth Drape
temporal disorder
Faucet
temporal disorder
Cloth Waving
temporal disorder
Droplet Fall
temporal disorder
Pendulum Oscillation
temporal disorder
Pyramid Impuct
temporal disorder
River Flow
temporal disorder
Moving Shadow
temporal disorder
Orbit Shadow
temporal disorder
Spotial Continuity
Ball Collision
telepertation
Ball Drup
teleportation
Block Slide
teleportation
Cloth Waving
flag teleport
Pyramid Impact
teleporting spheres
River Flow
invisible wall
Faucet Hlow
teleporting fluid
Conservation Energy
Ball
hounce momentum amplification
Collision
Collision
phantom force
Drop
dynamic scaling clastic explosion self attracting
Cloth Waving
Droplet Fall
Pyramid Impact
momentum multiplication
Droplet Fall
nca-consevvaion momentum
Conservation of Mass
Dropett Fall
non conservation fluid
Droplet Fall
matter creation
Droplet Fall
negative viscosity
Fancer Flow
fracturing fluid
Droplet Fall
phase shifting fluid
River Flow
conservation fluid
Geometric Imaariance
Ball Collision
size change
Block Slide
size changing
Pyramid Impact
phuse shifting
Pyramid Impact
sphere fusion
Orbit Shadow
varying size
Optical Consistency
Moving Shadow
inverted shadlow
Moving Shadow
shadow
Moving Shadow wrong shadow shape Moving Shadow
no object
Orbit Shadow
impossible reflection
Response
Cloth Drape
impossible folding
Cloth Drape
rubber cloth
Cloth Drape
ground penetration
Block Slide
hovering
Block Slide
inregular motion
Block Slide
jittering
Cloth Waving
flag shatter
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Table 6: IntPhys Dev (01/02/03) with LikePhys. We report PPE (%, lower is better) for each block and the average across blocks.
Model
Avg.
Animatediff SDXL
45.8
59.2
48.3
51.1
Wan2.1-T2V-144
48.3
51.7
52.5
50.8
LTX v0.9.5
46.7
49.2
46.7
47.5
ModelScope
52.5
49.2
47.2
ZeroScope
42.5
48.3
50.8
47.2
Videox
43.3
49.2
46.7
46.4
Animatediff
45.0
43.3
43.3
43,9
CogVidcoX
40.0
45.8
45.0
43.6
Hunyuan T2V
35.8
30.0
25.8
30.5
F VISUAL SAMPLES
We further provide visual samples in the constructed benchmark for each scenario as mentioned in Apx. B. As shown in Fig. 7, Fig. and Fig. 9. We show one valid and two invalid cases for each scenario for illustration.
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Valld
Teepprration
valls
Callision
Telpprraatoon
Valdd
Penduljum Oscillation
Fath Genterton
Valid
Teleportaation
Figure 7: Visual samples from Ball Drop, Ball Collision, Pendulum Oscillation, and Block Slide scenarios.
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Published as a conference paper at ICLR 2026
Varte
Drog
Concevatiion
Dlocddr
Valle
Fap Flow
uaidd
Drape
valid
Figure 8: Visual sample from Droplet Fall, Faucet Flow, Cloth Drape, Cloth waving scenarios.
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Published as a conference paper at ICLR 2026
Riger Elow
valld
Moving, Shadow Seaatieo
Me CApeet
unidd
Impace
Piese Shitnng
valua
Orbii Shadow Dasipp posppeaiiie
Figure 9; Visual sample from River Flow, Moving Shadow, Pyramid Impact, Orbit Shadow scenario.
23


Reviews:
[{"review_id": "g95hP3rGHM", "reviewer": "Reviewer_hTWK", "summary": "This paper introduces LikePhys, a training-free, likelihood-preference-based evaluation method designed to assess the intuitive physics understanding of video diffusion models (VDMs). The core motivation is to address the challenge of disentangling physical plausibility from visual appearance in generated videos—an issue that existing evaluation methods often fail to handle due to biases from visual fidelity or subjective judgments. The key contribution of this work is the development of a new evaluation framework along with a comprehensive benchmark.", "questions": "- The benchmarks were simulated using Blender. Which physics engine did you use, and what is the duration of each video?\n\n- In Figure 3, the textures appear relatively simple. Have you included more complex textured objects?\n\n- I feel that this approach is somewhat similar to Direct Preference Optimization (DPO). Could the author please elaborate on this point?\n\n- If the model not only trained on realistic videos, how to adapt your work to evaluate this model?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- This work is the first to utilize diffusion model likelihoods for evaluating intuitive physics. The approach of using the denoising objective as a likelihood surrogate for a \"violation-of-expectation\" test is both clever and well-justified, as it effectively examines the model's internal representation of physical concepts.\n\n- Experimental results indicate that the proposed Physics Perceptual Evaluation (PPE) aligns more closely with human preferences compared to existing automatic metrics.\n\n- The benchmark is comprehensive, providing a valuable framework for evaluating these models.", "weaknesses": "- The primary concern is that the method's validity depends on a curated set of synthetic simulations. While controlled violations are necessary, this raises questions about how well the findings generalize to the distribution of real-world, natural videos.\n\n- As stated in the paper, this work assumes the learned distribution is physics-plausible, which is actually infeasible for large video diffusion models.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "HpsUnum2Bw", "reviewer": "Reviewer_Bi5p", "summary": "This paper proposes a very simple approach to eval video diffusion models' physical plausiblity, named Plausibility Preference Error (PPE). PPE is a pair-wise eval metrics, by calculating the error between the loss of denoising physically valid and impossible videos.", "questions": "1. For timestep selection, different models are using different weighting scheme, having differnt density of timestep sampling when doing training, so for the timestep selection, does it makes sense to sample uniformly? or randomly sampled? Is such sampling strategy robust enough?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 1, "strengths": "1. This eval method is easy to understand and implement, the motivation is clear.\n2. The authors provide good analysis on Disentanglement of Visual Appearance, convinced me that PPE is mainly for physical plausiblity, and are merely affected by visual quality.\n3. The authors provide good discussions for model size, number of frames, CFG, data scale, which provides useful insights.", "weaknesses": "1. This approach needs to access model params, which makes it impossible for evaluating closed-source video diffusion models. This is also discussed in Section 5\n2. All of the scenarios are from simulator, will the models pretrained more on simulated data have advantages over other models? I see some discussion in Section 5, the authors rely on an assumption that the models are mostly trained on real-world recordings rather than\nanimated or synthetic content. But I doubt this may not hold.\n3. One of the most important motivation of this paper is \"VLM based methods fail to disentangle physics from visual appearance.\", while the authors do not provide a correlation between these scores and visual quality metrics like In Table 3. I felt this is important to justify the necessity of using PPE", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "awQezgzGqZ", "reviewer": "Reviewer_tiXF", "summary": "The problem studied in this paper is how to evaluate a video generation model w.r.t. obedience to physical rules, under the setting that the model weights are accessible. The proposed method is train-free/zero-shot: 1) synthesize benchmark videos with Blender with two sets, $V+$ for physical feasible videos and $V-$ for physical infeasible videos; 2) calculate noise estimation errors for videos in $V+$ and $V-$; 3) calculate how many times the video model would estimate lower errors for videos from $V+$ videos from $V-$, then normalize the score to get the so called \"Plausibility Preference Error\" score (page 4, line 211~213).  \nWith the proposed method, authors benchmarked 12 open-sourced video generation models, and compared its correlation with human as well as VLM judgements.", "questions": "1. About the mismatch between the assumption and the actual calculation of PPE: in page 1, line 52~53, authors stated the essential assumpation *we use a simulator to render paired videos. In one, we render physically-realistic phenomena, while in the other we introduce \na controlled violation of physics. We keep the visual appearance consistent in the pair, ensuring that any difference resulting from processing the videos can be attributed solely to the breach of physics principles*, which means the video $x^+$ and $x^-$ should have the same prompt and scene content. But from the eq.5 in page 4 and appendix A in page 14, it seems non-paired videos $x^+_j$ and $x^-_k$ are also included in calculation. Did I get anything wrong or the presented calculation is problematic here?\n2. Distribution discrepancy among the benchmark videos and tested models: from the demo frames in Fig.1 and appendix page 20~22, it seems the synthesized video from Blender are over simplified, with non-realistic looking and blank background. This distribution would be definitely far from the trainset of video generation models tested in the paper. Though results in Table 2 show that the method proposed is better than Qwen2.5 VL and VideoPhy1/2, I doubt whether this is fair for models with less training data similar to the synthesized video from the paper. From Table 1 in page 6, it seems the calculated PPE scores fluctuate dramatically among the tested 12 physics categories for any of the 12 models listed there, and I doubt the distribution discrepancy might be the hidden problem.\n3. Following question 2, authors didn't specify which one from the Qwen2.5 VL model family is used.", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "Although the work is still crude in its current form, it provides an insightful view angle for video model evaluation, with its train-free method.", "weaknesses": "The idea of evaluating an image/video generation model's certain capability with its noise estimation error is not new, though this is the first work I met for physical video generation.  Plus there's some essential problems with the proposed method unclear from the paper, which is stated in the following \"Questions\" section.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "mYaB0rsKA3", "reviewer": "Reviewer_y5Dy", "summary": "This paper introduces LikePhys, a training-free evaluation framework designed to assess the intuitive physics understanding of video diffusion models (VDMs). Instead of relying on human or vision-language judgments, LikePhys measures how well a model distinguishes physically valid from invalid videos using its denoising loss as a likelihood proxy. The authors construct a controlled benchmark of twelve simulated scenarios across four physics domains—rigid-body, continuum, fluid mechanics, and optical effects—and define the Plausibility Preference Error (PPE) to quantify whether the model assigns higher likelihood to physically plausible sequences.\n\nThrough systematic experiments on twelve state-of-the-art VDMs, the study finds that larger Transformer-based models (e.g., Hunyuan T2V, Wan 2.1–14B) outperform UNet-based ones, showing partial emergence of physics reasoning. PPE correlates strongly with human judgments but remains largely independent of visual quality metrics, confirming that it measures physical plausibility rather than appearance. Nonetheless, current models still struggle with complex or chaotic dynamics—particularly in fluid mechanics and conservation-law scenarios—highlighting the need for future work on physics-aware training and longer temporal modeling.", "questions": "I do not see evaluation code and data in the supplementary material. Do you have any opensource plan?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper proposes LikePhys, a novel and training-free method that evaluates intuitive physics understanding through a model’s own likelihood estimation. This approach elegantly connects diffusion models’ denoising objective with physical plausibility assessment, avoiding dependence on human annotation or vision–language model judges. It offers a fundamentally objective and interpretable evaluation paradigm for generative models.\n2. LikePhys can be directly applied to any diffusion-based video model without additional training or fine-tuning. By using the denoising loss as an ELBO-based likelihood surrogate, it remains compatible with a wide range of architectures and inference setups, enabling scalable and reproducible benchmarking across different models.\n3. The authors construct a highly systematic simulation dataset of twelve physics scenarios covering four domains—rigid-body, continuum, fluid mechanics, and optical effects. Each valid–invalid pair is carefully designed to isolate specific physics violations (e.g., energy, mass, continuity) while holding appearance constant, ensuring the evaluation reflects genuine physics reasoning rather than visual bias.\n4. The proposed Plausibility Preference Error (PPE) metric quantifies the proportion of cases where a model fails to prefer physically valid samples. It is intuitive, numerically stable, and easily comparable across models. Moreover, the authors demonstrate that PPE aligns strongly with human judgments of physical consistency while being independent from standard visual quality metrics, confirming its validity and specificity.", "weaknesses": "1. A key limitation of the proposed likelihood-preference framework lies in its implicit assumption that higher estimated likelihood (i.e., lower denoising loss) reflects stronger physical understanding. In practice, the likelihood assigned by a diffusion model is influenced by many confounding factors beyond physics correctness. For instance, if a video sample—whether physically valid or invalid—resembles patterns frequently seen in the training data, it may naturally obtain a higher likelihood simply due to data distribution similarity, rather than genuine adherence to physical laws. Conversely, both valid and invalid videos that deviate from the model’s training distribution could be assigned uniformly low likelihoods, making the difference between them statistically insignificant. As a result, the LikePhys metric may sometimes conflate distribution familiarity with physical plausibility, leading to inaccurate or unstable evaluations, especially when the model exhibits strong dataset bias. This issue raises questions about the robustness and interpretability of using likelihood differences alone as a proxy for intuitive physics understanding.\n2. Although the benchmark covers four major physics domains, it remains synthetic and controlled, relying solely on Blender-rendered simulations. While this ensures experimental rigor, it limits the method’s ability to generalize to real-world, noisy, or unstructured videos, where visual complexity, uncertainty, and imperfect physical consistency are common. The results might therefore overestimate models’ real-world physics reasoning ability.\n3. The metric treats intuitive physics understanding as a pairwise likelihood preference problem, which captures surface-level plausibility but may fail to reflect causal reasoning, temporal prediction, or long-horizon dynamics that are essential for deeper physical understanding. As a result, models that memorize motion patterns could perform well on LikePhys without genuinely learning the underlying physical principles.\n4. While the paper reports domain-level PPE scores, it provides limited qualitative analysis or visual diagnosis of why certain models fail under specific laws (e.g., temporal continuity or conservation of mass). More detailed case studies or ablation examples could have strengthened interpretability and clarified whether errors stem from architecture limitations, data bias, or diffusion noise modeling.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)