Hierarchical Fine-grained Preference Optimization for Physically Plausible Video Generation
Authors: Harold Haodong Chen
Haojian Huang
Qifeng Chen
Harry Yang
Ser-Nam Lim
Venue: NeurIPS 2025 poster ()
Keywords: Direct Preference Optimization
Video Generation
Primary area: applications

Abstract: Recent advancements in video generation have enabled the creation of high-quality, visually compelling videos. However, generating videos that adhere to the laws of physics remains a critical challenge for applications requiring realism and accuracy. In this work, we propose **PhysHPO**, a novel framework for Hierarchical Cross-Modal Direct Preference Optimization, to tackle this challenge by enabling fine-grained preference alignment for physically plausible video generation. PhysHPO optimizes video alignment across four hierarchical granularities: a) ***Instance Level***, aligning the overall video content with the input prompt; b) ***State Level***, ensuring temporal consistency using boundary frames as anchors; c) ***Motion Level***, modeling motion trajectories for realistic dynamics; and d) ***Semantic Level***, maintaining logical consistency between narrative and visuals. Recognizing that real-world videos are the best reflections of physical phenomena, we further introduce an automated data selection pipeline to efficiently identify and utilize *"good data"* from existing large-scale text-video datasets, thereby eliminating the need for costly and time-intensive dataset construction. Extensive experiments on both physics-focused and general capability benchmarks demonstrate that PhysHPO significantly improves physical plausibility and overall video generation quality of advanced models. To the best of our knowledge, this is the first work to explore fine-grained preference alignment and data selection for video generation, paving the way for more realistic and human-preferred video generation paradigms.

Hierarchical Fine-grained Preference Optimization for Physically Plausible Video Generation
Harold Haodong Chen Haojian Huang 1.2 Qifeng Chen Harry Yang Ser-Nam Lim f3.4
'hee Hong Kong University of Science and Technology (Guangzhou) "The Hong Kong University of Science and Technology
Everlyn AI, UUniversity of Central Florida
iCorresponding Author haroldchenS23@@mail.comm
Abstract
Recent advancements in video generation have enabled the creation of high-quality, visually compelling videos. However, generating videos that adhere to the laws physics remains critical challenge for applications requiring realism and accuracy. this work, we propose PhysHPO, a novel framework for Hierarchical Cross- Modal Direct Preference Optimization, tackle this challenge by enabling fine- grained preference alignment for physically plausible video generation. PhysHPO optimizes video alignment across four hierarchical granularities: a) Instance Level, aligning the overall video content with the input prompt; b) State Level, ensuring temporal consistency using boundary frames as anchors; Motion Level, modeling motion trajectories for realistic dynamics; and d) Semantic Level, maintaining logical consistency between narrative and visuals. Recognizing that real-world videos are the best reflections of physical phenomena, we further introduce an automated data selection pipeline efficiently identify and utilize "good data" from existing large-scale text-video datasets, thereby eliminating the need for costly and time-intensive dataset construction. Extensive experiments on both physics- focused and general capability benchmarks demonstrate that PhysHPO significantly improves physical plausibility and overall video generation quality of advanced models. To the best of our knowledge, this is the first work to explore fine- mmaigrained preference alignment and data selection for video generation, paving the way for more realistic and human- aeean--eeferred video generation paradigms. PhysHPO Page
Introduction
Video generation has recently achieved significant strides in producing high-quality, visually com- pelling [39] 73 90 99 64), and lengthy [28 19, 60, 96] videos depicting real-world scenarios. Despite these ad vdvncemenss, generating videos that adhere to the laws of physics remains challeng- ing and critical research problem. The ability to create physically plausible videos essential for applications ranging from virtual reality to simulations, where realism and accuracy are paramount.
Current efforts to enhance the physical fidelity of text-to-video (T2V) generation can be broadly categorized into test-time reflection-based optimization and training-tims tuning-based optimiza- tion. Test-time reflection-basee methods, such as PhyT2V [85], employ a large language model (LLM) to iteratively refine initial T2V prompts. While effective, these methods significantly decrease computational efficiency and are inherently limited by the upper bounds of the model's capabilities in self-correction paradigms. Conversely, recent tuning-based methods (e.g., WISA [75] and Syn Video (98) focus on traditional supervised fine-tuning (SFT) paradigms. Although effective, SFT relics
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Baseline
PhysHPO
Baseline
PhysHPO
Baseline
PhysHPO
Figure I: PhysHPO significantly improves the physical plausibility of video generation. Text prompts are adopted from VideoPhy (6) (Top) Fluid-Fhiie Honey diffusing into warm milk. (Middle) Solid-Fluid An apple falls into a vat of red wine. (Bottom) Solid-Solid: Peeler peels an apple.
heavily on fixed supervisory signals, which show suboptimal effectiveness when targeting specific capability optimization 51, 23).
Emerging post-training techniques like Direct Preference Optimization (DPO) 61 have demonstrated more efficient pair-wise optimization paradigms discern differences between human--refenme and non-preferred samples, enhancing specific aspects of visual generation models, e-g., safety [63] 50), customization [4). super-resolution [9]. This indicates DPO holds potential for physically plausible video generation, yet remains largely underexplored. However, recent DPO works in video generation [51 94 36 16 predominantly focus on coarse-grained alignment between videos at the instance level, which may result in suboptimal preference alignment (31. We emphasize that achieving optimal alignment, particularly for physically plausible video generation, necessitates fine-grained preference alignment that goes beyond visual appeal to incorporate detailed modeling.
Building on these insights, we propose novel framework, Hierarchical Cross-Modal Direct Prefer- ence Optimization for physically plausible video generation, namely PhysHPO, PhysHPO enhances video preference alignment across hierarchical granularities. Specifically, we design four levels of alignment: I Instance Level: Ensuring comprehensive alignment by matching the overall prompt content with the most suitable video. State Level: Leveraging boundary frames as critical anchors for establishing plausible states. @ Motion Level: Modeling motion through structural information within videos, enabling alignment beyond mere pixel appearance. I Semantic Level: Ensuring logical consistency between what is described and what is visually portrayed.
Moreover, compared to existing SFT methods [75 98 for generating physically plausible videos. which consume extensive resources in dataset construction, we argue that the popular "One-Model- One-Daten Dataser" paradigm may not be optimal. Unlike tasks like condition-guided (e.g., dancing 103 30 or style-focused (e.g., cartoon 82 19 video generation, which demand additional data annotations or domain- -specifcc data, real-world videos inherently encapsulate rich physical dynamics, suggesting potential for more efficient data utilization. To this end, we propose novel automated data selection pipeline to efficiently process existing large-scale text-video datasets, circumventing exhaustive new data collection efforts for physically plausible video generation. Unlike existing data processing pipelines for high-quality video large-scale pre-training (e.g., Open-Sora (99). our key idea is to select subset of good data" from large, high-quality raw data that closely matches desired

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target -ntiunetittiiutiivvlly.... real-world videos where physical laws are prominently reflected. To the best of our knowledge, no prior work has explored data selection in the domain of video generation. To summarize, this work contributes in threefold:
We introduce a novel Hierarchical Cross-Modas DPO (PhysHPO) framework for video generation, more fine-grained DPO strategy to enhance alignment between videos, optimizing for physically plausible video generation.
We advocate leveraging real-world videos rather than constructing datasets from scratch for physically plausible video generation. This approach intuitively reflects physical phenomena and introduces the data selection problem to video generation for the first time.
Extensive experiments on both physics-focused (i.e., VideoPhy [6. PhyGenBench (54) and general
capability (i.e., VBench (34) benchmarks demonstrate that PhysHPO significantly improves the physical plausibility and overall video generation capabilities of existing advanced models.
Related Work
Physics-aware Video Generation
While generating visually compelling videos has advanced,
achieving physics plausibility remains challenging, as noted by users and benchmarks 6 38
56 42
88) in image-to-video (12V) focus on parsing objects from images and estimating their motion by considering physical properties, and Li et al. [1 explores solely object freefall. However, these methods are limited to fixed physical categories or static scenarios, which restricts their generalizability Recent works [85 75 98 aim to enhance the broader physical plausibility of T2V models. PhyT2V 851 introduces an LLM for iteratively prompt refinement during test time. However, the hugely increased inference overhead and inherent performance limitations restrict its effectiveness. Subsequent research has explored traditional SFT to improve model performance. Specifically, WISA 751 constructs 32K video dataset, and Syn Video 98 uses a computer graphics pipeline to synthesize video data, both requiring substantial manual intervention, which resource-intensive. Intuitively, real-world videos naturally reflect physical phenomena. Thus, efficiently utilizing existing datasets without unnecessary data inefficiency is an intriguing problem. To this end, we introduce the concept of data selection for video generation for the first time, automating the process to leverage available data resources effectively. While SFT has demonstrated superior performance in pre-training, we propose adopting the DPO post-training paradigm to further model differences between video pairs, enabling deeper exploration of physical information.
65). Existing works 40 55 52
Data Selection for "Good Data" Data selection is pivotal technique for efficiently training models without sacrificing performance K [44 as highlighted in both pre-training 7 70 and post-training 14 47) stages. Recent studies emphasize that the effectiveness of LMs stems from combination of large-scale pre- tre-training and smaller, meticulously curated instruction datasets [69] 18 77 101. Various sophisticated approaches have been proposed for LMs. including quality-
based [45 20 46). diversity-awarr 24 [3), complexity considerations 84 67 35), alongside simpler
heuristics like selecting longer responses and gradient-based coreset selection 80 95 59) collectively demonstrating significant benefits in LM training. However, existing methods predominantly target LMs, leaving their potential in video generation largely unexplored. In this work, we pioneer the exploration of data selection strategies specifically for video generation in the post-training phase. We propose a new automated data selection pipeline, explicitly emphasizing reality, physical fidelity, and diversity to enhance the generation of physically plausible videos.
Direct Preference Optimization for Video Generation DPO 61 has emerged as promising alternative to traditional RLHF (58) for enhancing LMs [I]
49 and image generative models [2 without needing an extra reward model. Recent efforts have applied DPO to video generation.
27
Pioneering work like ViO [5T) follows the Difttusion-DPOOO 72 paradigm, introducing OmniS- core for adaptive video scoring, while ViDPO (36) aligns outputs with human preferences using feedback. OnlineVPO g optimizes off-policy with a video-centric model, and MagicID 43 uses DPO for ID-conditionee customization. Cheng et al. [16] employs a discriminator-free free approach for direct optimization, and GAPO 102 introduces AnimeReward for anime video generation. Recent foundation models like Seaweed-7B 7B (64), SkyReels-V V2 [00 also incorporate DPO in post-training, further highlighting its potential. However, existing DPO works focus solely on coarse-grained alignment at the video instance level, overlooking finer details. In this paper, we aim to enhance DPO's effectiveness, specifically for generating physically plausible videos by modeling fine-grained alignment. To achieve this, we propose novel hierarchical cross-modal preference optimization framework, named PhysHPO, which effectively captures the fine- seee-graindd details of videos.
3

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3 Preliminaries
Direct Preference Optimization for Diffuusio Models Diffusion-DPO [72] adapts human pref- erence alignment to iterative generation processes, eliminating explicit reward modeling. Given diffusion model po(y|æ, i) and reference model Prer(y/a, (r, f), the denoising objective with preference constraints becomes
max E (r y,t)
t)] - 3DxL (pe(v) æ t) Prer t)),
where r(-) denotes the time-dependent reward function, x the input condition, y the generated sample, and the timestep. DPO establishes a trajectory-Ievel reward mapping through denoising paths:
r y, t) 3 log Po(y) t) + 3 log (a,
Pret (x,))
where B controls KL constraint strength, and (xx, t) the denendeennnn partition function.
The preference optimization objective is derived by substituting the reward parameterizatton and applying negative log- likelihood loss:
CDO - -I, WDD log (u(x, yw 21, t)), log po(yulzztt) Prof yeu (x,t)
(yll 2. Prof (1)) x
log
(3)
Preferred puth soure
Non preferred path score
Data Selection: From Physical to Diverse
Existing text-video datasets have already been rigorously screened for quantity and quality 999 57
(8] While creating specific datasets for different tasks is popular [93 33 19 92]. for physically plausible video generation, real-world videos inherently reflect physical laws. Thus, selecting existing data may be more optimal than constructing new datasets [75 98. In this section, we present an initial exploration of the data selection in video generation, focusing specifically on identifying "good data" that effectively reflects physical laws.
4.1 The Data Selection Problem
Video generation models undergo pre-training to learn world knowledge R 2 Fine-tuning these
models aligns them with specific goals, analogous to instruction tuning in LMs 91. Data selection
has proven effective for aligning LMs by identifying a small, high-quality dataset [53 8 25 80). Consider large data pool D {I1, I2, Jnn. where each data point æ; = (V₁, C.) consists of a video V; and its corresponding caption C, The objective to select a subset ((m) of size m that maximizes the post-training performance P:
S" arg max (()(m)).
While there are various potential ways for data selection in desired domain, goal is keep the process as simple as possible to be practical, as shown in Figure 2 (Left). (Left). We next detail the process.
4.2 Selection for Real-World Physics
Reality Given a large-scale high-quality text- exte-video dataset
Table Accuracy of two-stage real-
D (we adopt Open VidHD-004M [57] this work, which is
ity selection with 1, 000 samples.
widely utilized for post-training 112
87 68 13], the first step
Reality Selection
Acc (%)
to filter out real-world videos into real-world data pool D'. Specifically, considering the significant content differences between real-world and virtual videos, we employ a vision-
Qwen2.5-VV LL [4]
99.6
DeepSeek VL2 77)
100.0
language models (VLMs) (i.e., Qwen2.5-VL [4]] to determine whether video V, is real-world video. We further task DeepSeek- VL2 [79] for a double-check to ensure the accuracy. Table shows the accuracy from random sample of 1,000 videos from D.
Physical Fidelity To align with previous works, we adopt 17 physical phenomena across three physics categories from WISA [75] 1 Dynamic (rigid body motion, collision, liquid motion, gas motion, elastic motion, deformation), liquefaction, combustion, explosion), and @ Optic (reflection, refraction, scattering, interference and diffraction, unnatural light source). Unlike widely used heuristic classification- data selection methods in LMs that require a target dataset for reference [8] 21), we follow the popular employ LLMs for automatic data evaluation.
Thermodynamic (melting, solidification, vaporization,
LLM-as-a-Judge paradigm [53] 26 102

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Data Selection
Hierarchical Cross-Modal Preference Optimization
Raw Data
Prompt:
Masked
Enatesction
MYos real-world
Semantic Level
Ref Model
Reality Selector
Caption: C,
Structure, 31
mottion
Error Video
811
Video
State Video dish
Evolving Dynamic Prompt
V1
Bi
Dynamic Seorc:
Mation Level
Instance Level
State Level
Physical Fidelity Selector
Policy Video Diffusion Model
Diversity
Reference Video Diffusion Model
Figure 2: The overview of our proposed (Left) data selection and (Right) PhysHPO framework.
To minimize consumption, we then perform selections at the caption level. Given a video caption Cr naive way is to task an LLM with evaluating whether the sample clearly reflects physical laws by directly assigning a score. However, LLMs might assign similar scores to most samples due to the lack of references [32] To address this, propose adopting in-depth evolving prompting [84] to augment captions. Unlike previous works [53 [4] that augment samples a single dimension (e.g., complexity), we explore multiple dimensions. Specifically, illustrated in Figure
first use the crafted evolving-dynamic prompts with an LLM (i.e., Qwen2.5 (86)) to augment the caption, emphasizing each of the seven physical phenomena in the "Dynamic" category. This helps the model distinguish differences in captions more precisely, achieving fine-grained scoring. We then ask the samples, obtaining the "Dynamic" scores 8y corresponding the
L.M to rank and score these caption C. Similarly, the "Thermodynamic" score and "Optic" score s, can be obtained. This strategy provides a more nuanced distinction of physical phenomena. Details in Appendix $B
The total score is calculated as s = Sd x St x So. Then all samples in D' are sorted based on s, resulting in the sorted pool S' = {21, 22, xx4. where 2 is the sample with the highest score.
4.3 Selection for Diversity
To ensure an advanced generation model can handle varied user prompts, it's desirable for data maintain maximum diversity within a given budget m.. However, real-world data often exhibits redundancy [1] To this end, we introduce an iterative method to ensure diversity in selected real-world data following Deita [53]. Briefly put, the key idea is iteratively select samples x from the data pool S", adding them to the dataset S only if they contribute its diversity. Formally.
Dascine
Seact Sextiag IS13-O00 tats 22100-000 Data
kntt D20
Danden 12100e00
PCA Siore (%)
60-
we define an indicator function 1 [[F(,,, which is 1 if the diversity criterion F(z S') met, otherwise Using LLaMA-1 13B (71) captions are encoded into embeddings, and cosine distance is calculated between each candidate sample and its nearest neighbor in S. A sample contributes to diversity d < T, with 7 = 0.9.
60-
45
40
We compare the performance of different data selection strate- gies with vanilla DPO 77], along with our PhysHPO, demonstrated in Figure 3 Several key observations are summa- rized: Obs. O Superiority of Data Selection. Our data (21K). selected for reality and physical fidelity, demonstrates superior quality compared to other strategies like direct score, random selection, or even the manually constructed dataset WISA-32K 775 and unselected raw data (433K). "Our Data (21X)+DD) +DPO" outperforms "Direct Scoring (21K) +DPO", "Random Selection +DPO", -DIS--3 and "Raw Data (433K)+DPD1 highlighting effectiveness of our selection. Obs. Importance of Data Diversity. The comparison between "Our
Machannter
Themral
Materiale
Onaal
Figure 3: Performance comparison
of different data strategies with our PhysHPO on PhysGenBench [54].
5

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Data (21K)+DPO" and "Ours w/o diversity (69K)+DPD" underscores the critical role of diversity in data selection. Despite fewer samples, the diverse dataset outperforms the larger one, indicating that diversity enhances model capability. Obs.0 Strategic Data and Optimization Synergy. The integration of our selected data with PhysHPO ("Our Data (21K) +Ph))+POHH))"" achieves the high- est performance, illustrating the powerful synergy between strategic data selection and advanced optimization. More analysis is provided in Appendix $B
We next detail our PhysHPO framework.
5 Methodology
To tackle the challenging physically plausible video generation, we propose PhysHPOO, which imple- ments hierarchical preference optimization across four levels (Figure 2 (i) Instance-level Overall Preference Optimization, aligning video-level preferences to ensure overall quality ($5.1); (fi) State- level Boundary Preference Optimization, anchoring physical states at the start and end frames for stability ($5.2); (iii) Motion-level Dynamic Preference Optimization, utilizing structural informa- tion to accurately model and align motion representation ($5.3); (iv) Semantic-level Consistency Preference Optimization, ensuring fine-grained coherence between the narrative and visuals ($5.4..
5.1 Instance-level Overall Preference Optimization
Unlike VideoDPO [ST] which relies solely on its base model for preference pair video data (Nues Vt). limiting optimization to the model's capabilities, our approach leverages data selected through our data selection process as the preferred video Yu and its text prompt z. For the non-pre con-prefereed video yr. recent works [122 944 often generate these using the base model based on z, adhering Eq. as the objective function. However, complex dependencies in non-preferred videos are often overlooked. Typically, video generation faces two main issues: ① Imperfections or errors physical aspects, e.g. motion; @ Failure to fully express the prompt's s information, e.g., missing objects. To mitigate this, we introduce two types of non- con-preferred videos into the optimization process:
po po jr, ()
po po (2,, 1)
Linstance
-Z log a
BBlg
33 log
Pret (Y (ye
Pret (31 (1) (z, t)
where
po(y J2, t) Pret x)
B, log (o(vil,, :Eferr.gap)
log
Σ
(6)
Pret Pre((1/12z,) (2,))
Here, yfr denotes the error-prone or imperfect non-preferred video that aligns semantically with the preferred sample. In contrast, yg"h represents non-preferred videos with semantic differences from the preferred sample. Specifically, for constructing yf" we generate three videos per using the base model and select the one most visually similar by similarity calculation the preferred video yo as the rejected video yf"r. For "fo,, we introduce random masking in prompt x during generation to create semantic differences from Vie
5.2 State-level Boundary Preference Optimization
While the instance-level alignment captures overall content, fine-grained alignment is essential for generating physically plausible videos, which previous approaches have often overlooked. In video generation, maintaining consistent physical state from the beginning to the end of a sequence is essential for ensuring realism and coherence. The initial and final frames are particularly critical as they anchor the video's 's physical narrative. To this end, propose the state-level boundary alignment to enhance the model's 's focus on the physical states at the start and end of videos. Specifically, we replace the first and last N frames of the preferred video you to construct the state-level non-preferred sample yf statee The state level objective function can be defined as
Statte
log σ (UState (x, Bwo vi stote 1))
5.3 Motion-level Dynamic Preference Optimization
While the visual appearance of videos is crucial for quality, excessive focus appearance may obscure the model's alignment with physical dynamics. To address this issue and enhance the model's 's learning of dynamic information such as motion, we propose the extraction of structural information (e.g., optical flow) from both preferred you and non-preferred yr (specifically yfrr) videos. These structural features, commonly used in condition-guided video generation [3 22 100]. allow for a more explicit representation of the physical motion differences between preferred and non non-pre non-preferred samples. Following Eq 77. the motion-level objective function MMttoon is derived

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Table 2: Evaluation on physics-foeused benchmarks, i.e. VideoPhy [6. PhyGenBench [54]; and general quality benchmark, i.e., VBench [34]. Vanilla DPO is implemented with selected data. We bold the best results, and "1" denotes that higher is better.
VideoPhy
GenGeenench [54]
VBench
Method
SS1
SEt
FFT
Over:t Mechanies"
Optiest
Thermalf Materials? Overall
Total?
Quality?
Semantic?
CogVideoX 2B [90]
12.7
21.9
25.4
18.6
0.38
0.43
0.34
0.39
0.39
81.6
82.5
77.8
+ PhyTTV [55
14.1
19.9
28.6
18.9
0.45
0.48
0.34
0.50
0.45
82.0
82.9
78.4
Vanilla DPO
72
15.5
19.2
28.6
19.2
0.43
0.50
0.40
0.50
0.46
82.2
83.1
79.0
PhysHPO (Ours)
20.4
24.7
42,9
25.9
0.50
0,56
0.47
0,58
0,53
82.5
83.3
79,3
CogVideoX-5B XX
24.4
53.1
43.6
39.6
0.39
0.55
0.40
0.42
0.45
81.9
83.1
77.3
+ PhyT2V
25.4
48.6
55.4
0.45
0.55
0.43
0.53
0.50
82.3
83.3
78.3
Vanilla DPO
28.2
50.0
51.8
0.48
0.60
0,47
0.58
0.54
82.4
83.3
78.7
PhysIIPO (Ours)
32.4
54.1
58,9
45,9
0.55
0.68
0.50
0.65
0.61
82.8
83.7
79.3
from "MMotion (I, Vw > Viu
ymmttonn U1 yi /motion 1), where y motion and i/| mottoon represent structural
information extracted from Yo and yt, respectively.
5.4 Semantic-level Consistency Preference Optimization
Most prior DPO works in diffusion models primarily rely visual alignment for optimization. However, we propose leveraging language to describe visual differences more precisely, providing specific referential information at the textual semantic level. Inspired by DPO in LMs [61, introduce the semantic-level consistency alignment by further modeling the semantic information of videos the textual level. Specifically, for each preference video pair yoo and yt, we first consider the prompt x as the preferred caption Cu-- A VLM (i.e., Qwen2.5-VL. (4) is then tasked to modify ca based on yı, adjusting the inconsistent parts generate qt This process ensures that ew and ej only differ in textual descriptions where the videos are inconsistent, thereby facilitating more targeted optimization. The USemantic (Cw, Cts ywu t) within SSeanntic then formulated as:
po (B /Cw, t) Pret (Vw Cwv t) Pref(yulet,
pe (yo [1,
USemantic
log
log
(8)
By integrating instance, state, motion, and semantic- preference optimization, the overall loss function for PhysHPO is defined as follows:
LPhysHPO Linstance ALsiate + PLMotion J.SSemantic
where X, p. and μ denote the loss weights.
6 Experiments
In this section, we conduct extensive experiments to answer the following research questions: (RQ1) Does PhysHPO enhance the physical plausibility of generated videos? (RQ2) Does PhysHPO compromise other performance aspects? (RQ3) How sensitive is PhysHPO to its key components? (RQ4) Is PhysHPO more advantageous than SFT for efficiency, effectiveness, and generalizabiltty?
6.1 Experimental Settings
Baselines We apply PhysHPO to the advanced models, CogVideoX-2B and 5B [90]. Due to the
unavailability of open-sourced code and model weights for SFT-based methods, i.e., WISA 75
and SynVideo 198], we focus our comparisons on the following baselines: PhyT2V (cVP9225)
85
vanilla DPO JCVPR
SFT [90), and the respective base models. Implementation and results on
Hunyuan Video
39 are shown in Appendix |
Evaluations To evaluate the effectiveness of PhysHPO, we adopt benchmarks focusing on two key
aspects: Physicx-focused: (i) VideoPhy [G] for interactions of solid-solid, solid-fluid, and fluid-fluid. (ii PhyGenBench [54] for mechanics, optics, thermal, and materials. @ General Capability: VBench 34 for both overall quality and semantic.
Implementation Details We train base models on our selected dataset with global batch size of 8, using the Adam W optimizer and learning rate of 2e 5. Instance-level non-preferred weights set to Berr = 0.7 and 8gap = 0.3, with N = 2 for state-level samples. The loss weights X, p. and set to 0.4, 0.3, and 0.2, respectively. All experiments are conducted on 8 NVIDIA H100 GPUs.
6.2 Physical& & General Performance of PhysHPO
To answer RQ1 and RQ2, we comprehensively compare PhysHPO with three baseline methods on physics-focusee focused and general benchmarks (Table along with the qualitative results and user study shown in Figure and Figure 5 (Left), respectively. Our observations summarized as follows:
7

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Baseline
PhysHPO
Baseline
PhysHPO
Baseline
PhysHPO
Baseline
PhysHPO
Figure 4: Qualitative results of PhysHPO. Due space limitations, results of other baselines
provided in Appendix SD Text prompts are sourced from PhyGenBench (54), (Ist) Materials: delicate, fragile egg is hurled with significant force towards rugged, solid rock surface, where collides upon impact. (2nd) Mechanics: A vibrant, elastic tennis hall is thrown forcefully towards the ground, capturing its dynamic interaction with the surface upon impact. (3rd) Optics: A magnifying glass is gradually moving closer a coin, revealing the intricate details and textures of the embossed design as it approaches. (4th) Thermal: A timelapse captures transformation of milk in kettle as the temperature rapidly rises above 100'
Obs. PhysHPO demonstrates superior performance in enhancing both physical fidelity and general quality. As illustrated in Table our PhysHPO consistently outperforms baseline methods (i.e., P2y (85) and vanilla DPO (72), which exhibit minimal or even negative performance gains, three physics-focus benchmarks, each targeting distinct dimensions of physical plausibility. Table 2 further highlights its robustness on VBench (34] for general capabilities. Qualitative com- parisons in Figure D and Figure $
provide visual confirmation of PhysHPO's ability, showcasing clear advantages over the base model. Obs. PhysHPO effectively align video generation with human preference. Figure (Left) shows the user study conducted on both physical and general dimensions, where PhysHPO consistently outperforms baselines in aligning video generation with human preferences, demonstrating its superiority preference alignment.
6.3 Ablation Analysis
To answer RQ3, we conduct evaluations on VideoPhy 6 for the contributions of PhysHPO's each level and their combinations, as shown in Table We give the follow- ing observations: Obs.G Effectiveness of Hierarchical Preference Optimization. Table demonstrates steady performance decline as L.semantiss L-Motion and Lstate are progressively removed. This underscores the effectiveness
Table 3: Ablation study on level losses.
Method
SST
SFT
FFT
Over.
PhysHPO
32.4
54.1
58.9
45.9
wlo Cscaatic
wlo Creamic Estoiin
30.3
52.1
55.4
Only Einanes
28.9
50.7
51.8
41.9
Vanilla DPO Our Data
28.2
50.0
51.8
41.3

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OP
PhyGeBBench
100
01
Plyeio
Impasible Toumpta
MG
6A
5K
JoK
20K
40K
Baseline
SFI
PhystIP
VBench Overall
Preept Following
$2.9
Phyd
E40
Reparriaas Pronpes
PPO Mspotro
Pby72V
PhystUu
Figure 5: (Left) User study across five dimensions: overall preference, semantic adherence, physical commonsense, visual quality, and motion quality. (Middle) Performance comparison between PhysHPO and SFT under varying data volumes. (Right) Robustness testing with IPV-TXT [S]
"A piece of paper' >> "steam"
"A ceramic cup" >> "vanishes into thin air'
Baseline
Baseline
SFT
SFT
PhysHPO
PhysHPO
Figure 6: Robustness testing demonstration. Detailed prompts can be found in Appendix
of the hierarchical preference optimization framework, where each level contributes uniquely to improving physical fidelity. Obs. Importance of Fine-grained Alignment. Fine-grained alignment, both explicit and implicit, critical for optimization: i) The removal of instance-level gap video aligmment ("Only w/ Linetance" " vs. "Vanilla DPO w/ Our Data") highlights the importance of visu- ally explicit modeling in bridging gaps between generated and real-world semantics and dynamics. iî) The exclusion of LSmmantic reveals the necessity of implicit text-video alignment for capturing nuanced and coherent relationships between textual prompts and generated videos.
6.4 Analysis of PhysHPO SFT
To answer RQ4, we compare PhysHPO and SFT using the same data on PhyGenBench for physical fidelity and VBench for general quality, shown in Figure (Midddll) Inspired by (5), further as- sess both methods with impossible prompts (e.g., "A car drives through the ocean as if it were flying") evaluate whether improvements in physical fidelity also enable better handling of imaginative or physically impossible scenarios, with results presented in Figure (Right) and Figure 6 following observations: Obs. 3 PhysHPO is a data-efficient video generation enhancer. Figure (Middle) demonstrates that PhysHPO consistently outperforms SFT across varying data volumes in both physics-foeus focused and general quality metrics. This reflects PhysHPO's ability to achieve superior performance by leveraging training data more efficiently through fine-grained information utilization. Obs. O PhysHPO enables creative generalization beyond physical laws. As shown in Figure 5 (Right) and Figure 6 PhysHPO performs better on impossible prompts, generating videos that align more closely with semantic intent while maintaining internal consistency. This suggests that enhancing physical fidelity not only improves adherence to real-world physics but also equips the model with a stronger ability to generalize and adapt to imaginative scenarios, demonstrating learning beyond simple physical rules.
We give the
Conclusion
In this paper, we propose PhysHPO, novel framework for Hierarchical Cross-Modal Direct Preference Optimization, enhancing video fine- tne-grained alignment across four levels: instance, state, motion, and semantic. Recognizing real-world videos as the best reflections of physical phenomena, we introduce an automated data selection pipeline to efficiently utilize large-scale text-video datasets, removing the need for exhaustive dataset construction. Extensive evaluations on both physics-focused and general benchmarks demonstrate that PhysHPO significantly improves the physical plausibility and overall quality of existing video generation models, addressing key challenges in physically plausible video generation.

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Acknowledgement
This research was supported by the Innovation and Technology Fund of HKSAR under grant number GHX/05421GD.
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A More Results on Hunyuan Video
To further validate the superiority of proposed PhysHPO, in this section, further apply
PhysHPO on Hunyuan Video-540p (39] implemented by Fast Video [97].
VideoPPhy
n
PhyGenBench
S
VBench
34
Method
SST
SFT FF1
Over.t
Mechanies"
Opticst
Thermalf Materialst
Overall?
Totalf
Quality?
Semantic?
Honyuan
20
19.7 43.2 42.9
33.4
37,5
58.0
36.7
45.0
45.6
81.4
83.1
74.9
+ PhyT2V [5
21.1 45.9 50.0
36.3
40.0
58.0
33.3
47.5
46.3
80.5
82.0
74.5
+ PhyslIPO (Ours)
26.8 50.7 55.4
41.6
47.5
62.0
43.3
60.0
54.4
81.9
83.5
75.7
Table 4: Evaluation of PhysHPO on MinyuanViieeo [39]
Quantitative Results Table presents the quantitative results of PhysHPO on Hunyuan Video. Consistent with our Obs. in the main content, PhysHPO achieves superior performance in enhancing both physical fidelity and overall video quality. These results further validate its effectiveness.
Qualitative Results
We demonstrate results in Figure More results human action shown
Section D
Hunyuan Video
PhysHPO
"A bulldozer clears debris from construction site, moving it into dumpster."
HunyuanVideo
PhysHPO
"Multiple candles varying heights and widths are blown out simultaneously by single breath, some flames extinguishing faster than others." "
HunyuanVideo
PhysHPO
An apple submerging into water."
HunyuanVides
PhysHPO
volleyball is spiked, hitting the wooden floor of the court and producing a soundless bounce visible by its slight dip and subsequent rebound."
Figure 7: Qualitative demonstration of PhysHPO Hunyuan Video [39

---

B More Details of Data Selection
In-depth Evolving Prompting To achieve more fine-grained scoring for the physical fidelity
of real-world videos, we employ the in-depth evolving prompting strategy (84). This approach augments sample captions to emphasize specific physical phenomena. An example of our crafted evolving-dynant prompt is shown Figure
Evolving Dynamic Prompt
peoept: You Are an expert in physsce and natural language processing, specialsaing in anslyzing and enhancing taxtoss
descriptions nased on physicul phenonera, Your tase ia to process given (caption) ase pactom the tolioving acupи.
fel Step 1: Enbanes the captien based os aix phrsscal phenosss
For the given [esption], erbance it erghasise east of the folloking six dynazie ybesorena, 7he cahaneseest nust strictly be based on the original exptionte content. bo not jovect new evect, objects, or sceaarice Inetesd, fccus on rephrasing and siszorating on the exieting inforration the exstion higsiight the specitic characteriatien each presorenon, Raaure the unhunced captions vivid, preciau, and aligned with correspenting poysical benavior:
Rigid body action: translation.
ecerry. on aovenons solid objects zaintaia shage strecture,
socation
Reghantie
objeste
eschange
nightight espects cxption
tehaviors,
splasing,
sentiosed, stretching,
selevant oscsllatory
cleazez without natertale present
Slactic rotions
Elazorate
Righlight rake then vivie.
such Outout six enhanced captions, each tailored one the move phenemena. insuze that all enbancerents romiin faithdul to the original caption's concent.
Reformation:
stracture nsteriale
teleting, breaking.
*et Step 2: Store the antanced captions
Adter generating the six ersanced exptions, evsloate each caption bazed on how well it represents the correrpending dynance phesorancen Provide acore Iron to it poor, excellecti for eses capsion. Adciticnally, previde briet ceasce ter the sccre, expaiiinng shy the caption is effective or shoze it could be inproved.
t*t Piral Burget Fornat:
Your octpor chould follow tate exact fornat:
Caption rigid bedy nutions (Rnhanced caption]; Score: [1-5]; Resson: [Rxplanation]
Caption collision: [inhanced caption]: Beaze: [1-51: Ressons [#xplanation). caption liguid
[Rahanced caption])
(1-8)) [Rxplanation].
caption clastic [Rnbanced caption];
Csption ges
[Explanation..
[Rshanced caption])
(1-5)) [Explanation].
Caption deformacion: [Rohanced caption]: [1-51; [Rxplanation).
Figure 8: Demonstration of crafted -dynamii prompt.
Similarly, the evolvingg-herrmuuynnnii and evolving-optic prompts follow the same structure.
In addition the performance comparison between evolving- scoring and direct scoring shown Figure we also provide examples with detailed scores under different strategies in Figure
"In the video, persoe is seen preporing a mesl in kitcher, The persoon is using yellow whisk to mix ingrecientee i ailer pet The pat is placed on stove, which is hurned person is wecring the stroighfforwerd informative,
on The persin uaing a abaon that octively engrged providing clear view the cooking process.
contenta of in bazkground, thene is mecessory
captural
well-spupedd
oppeone
hand.
video
Dynomic Thammodyrancc Optic
Dynemic: 2.8: (rniid body merior: 4: collision: 2: liguid mation Bi metion: It elastic mot lon deformaciion: 4)
Thernadmmaiic L.E, (melting 2: puridiication reportatioon lique fection: comoustion: explasion 3] Optict 2.2 (rafiection: 3: refrection scathering innerf ference diffructinn unnoturd light aourcs
Direst Sooring
Erabiig Scrriin
captumci basisebbll player action, wearing ing a white jersey and yalloa accents, representing shoesssing shot. thir displays
Werrions The player is reveds pocked
demerstrating intercapt stolium. filled with enthusaaticc fans, adding the inturaily of the game. The video dynavic representation of the sport, highighhtng player is atfletiosm the exc tement of match."
Dynamic:44 Themmodyraaic
Dynomic: 1.0: (rigid body notion colleson: legul motion gos mat om elactic notion deformat Themsodyaaicc 3.0: tnetting solietficotion seporizutior: lquefoction combustion:: 1) (reflection refrection scattering interferance diffraction
Wil Direct Scating
Scoving Scoring
Figure 9: Comparison of direct scoring vs. our evolving scoring.
25

---

Specifically, we compare sample of dynamic stirring (Top) with another sample where the camera zooms but the scene remains static (Bottom). As shown, the direct scoring strategy assigns similar scores to these two distinct videos, while our evolving-based scoring provides more fine-grained differentiation and accurate assessments.
Analysis of Caption-based Selection In our data selection process, we first performed filtering to ensure reality, followed by caption- -assdd filtering enhance physical fidelity and diversity, Here, we further validate the efficiency and effectiveness of our caption- caption-based selection strategy.
Video
Vike
15443
170
2750 68%0
14000
12/8
Mensry Cunsumption
Hours thes)
1100
3153
300
Figure 10: Efficiency comparison of video- based selection vs. our caption-based selection. (Left) GPU Memory Consumption (GiB). (Middle) H100 GPU Hours (hrs). (Right) Cost (USD).
Figure T0 first demonstrates efficiency comparison of caption-based selection and video-based
selection. Specifically, O GPU Memory Consumption (Left): Caption-based selection requires significantly less GPU memory, reducing usage by 18.1% compared to video-based selection (15, 403 GiB vs. 18, 741 GiB). This reduction highlights the computational efficiency of our method in terms of memory footprint. @ H100 GPU Hours (Middle): Video- based selection increases compute time by 27. 67.6% (3, 410 hrs vs. 2, 736 hrs), indicating that caption- -based selection is more and better suited for large-scale processing. incur an additional $674.00 in cost compared to caption- cuption-based workflows, further emphasizing the cost-efficiency of our approach.
3 Cost (Righr): At scale, video-based workflows
To further validate the effectiveness of our caption- based selection, we randomly sampled 100 data points from the reality-selected data pool for human scoring. We then analyze the scoring accuracy by comparing human scoring results with three scoring methods: video-based scoring, caption-basse -bsed direct scoring, and our caption-based evolving scoring, As demonstrated in Figure [T] our caption-based based evolv- ing scoring achieves the highest alignment with hu- man scoring across all three evaluated dimensions. These results further confirm that our method not only improves computational efficiency but also ensures superior scoring reliability.
Dine:: Sooria
56-
Anounny (+tl (%)
8
Dynamac
Thermo-dssmmic
Upka:
Figure 11: Adjacency accuracy (+.33) of dif- ferent scoring strategies with human scoring.
Statistical Overview To provide clearer understanding
Table 5: Statistics of data selection.
of each stage in our data selection process, present the
Data Selection
Total Count
statistical breakdown in Table 5 The raw data sourced
Raw Data
433,523
directly from Open VidHD-0.44 [57]. Moving forward, we
+ Reality
266,931
will process more existing high-quality text-video datasets
+ Physics
59,076
(e.g., MiraData 377, Intern (76)) using our data selection pipeline to facilitate future research.
+ Diversity
21,085
Data Sample Demonstration Real-world videos serve as direct reflections of physical phenomena. Here, we present examples of our selected videos as illustrative examples, as shown in Figure [2] These examples highlight diverse physical scenarios, providing reference for the types of phenomena captured our dataset.
C More Experimental Settings and Analysis
C.1 More Details of Experimental Settings
Physics-focused Metric We evaluate the physical fidelity of generated videos using two widely adopted physics-focused benchmarks: VideoPhy [ICLR'25] [6] and PhyGenBench [ICML225] [54]
26

---

Figure 12: Randomly sampled data demonstration from selected dataset.
Following the evaluation protocol introduced in WISA [75], we leverage VideoCon-Physice from VideoPhy [ to assess two key metrics: Physical Law Consistency (PC) and Semantic Coherence (SA) of the generated videos. Specifically, we use 344 carefully curated prompts from VideoPhy and 160 prompts from PhyGenBench, both designed reflect diverse range of physical principles and scenarios.
To quantify performance, we follow the scoring criteria outlined in the respective benchmark papers. For both VideoPhy and PhyGenBench, we consider PC and SA values greater than or equal to 0.5 as PC and SA while values less than 0.5 are treated as PC 0 and SA = 0. Specifically;
For VideoPhy, we report the proportion of generated videos where both PC and SA 1, reflecting the alignment with physical laws and semantic intent simultaneously.
For PhyGenBench, focus exclusively on the Physical Commonsense Alignment (PCA) score, which evaluates the consistency of physical reasoning under the condition that PC
This evaluation framework ensures consistency with prior work and provides robust assessment of the physical fidelity and semantic coherence the generated videos.
Impossible Robustness Testing Inspired by Impossible Videos [ICML'25] ( we further employ prompts from IPV-TXT (5] to evaluate whether PhysHPO goes beyond merely fitting fixed physical phenomena and demonstrates stronger generalization and robustness. The results are illustrated in Figure (Right) and Figure
For the quantitative comparison shown in Figure 5 (Right), we follow the evaluation protocol to assess the generated videos on two key metrics:
Visual Quality (VQ): This metric is derived by combining six factors from VBench 34 including Subject Consistency, Background Consistency, Motion Smoothness, Aesthetic Quality, Imaging Quality, and Dynamic Degree. These factors are aggregated into a single score to reflect the overall visual quality of the generated videos.
Impossible Prompt Following (IPF): This metric evaluates how well the generated videos align with the semantic intent of the impossible prompts. Following (5), we utilize GPT-4o to provide a binary judgment for each video based on prompt adherence and calculate the proportion of positive judgments as the final IPF score.
For the qualitative comparison presented in Figure 6 we further detail the prompts here:
Left: "A piece of paper mysteriously transforms into steam on a wooden table. The surreal sequence begins with someone placing the paper down, followed by gentle touch from a human hand that triggers an unexpected reaction the solid paper instantly vaporizes into wisps of white steam that dissipate into the air."
Right: "A ceramic cup sitting plain table surface mysteriously vanishes into thin air in this photo-realistic footage. The abrupt disappearance happens without any visible cause, defying physics in uncanny way. The scene is captured in crisp, lifelike detail with natural lighting."
User Study We conduct a user study to evaluate human preferences using both the mean opinion score (MOS) and direct pairwise comparisons. Specifically, design user-friendly interface to
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facilitate the evaluation process and collect feedback from total of 15 volunteers. The detailed instructions provided participants are shown below.
User Study: Physically Plausible Video Generation
Thank you for participating in our user study! Please follow these steps to complete your evaluation:
Video Generation:
Carefully read the target prompt provided, and then view the provided
videos.
2. Scoring Criteria: Assign a score to each generated video based on the following aspects being the lowest, 5 being the highest):
Overall Preference: A holistic evaluation of your overall satisfaction with the generated video, including aspects such as semantics, physical reasoning, visual quality, and motion quality.
Semantic Adherence: How accurately the video reflects the input semantic description or textual instructions.
Physical Commonsense: Whether the video content aligns with basic physical common- sense, such as the laws of motion, object interactions, and logical behavior.
Visual Quality: The visual appearance the video, including resolution, clarity, color representation, and texture details.
Motion Quality: The smoothness and naturalness of motion in the video, including object or character trajectories and speed variations.
Submission: Click the "Submit Scores" button submit your scores.
Notations:
We observe that the edge browser is not fully compatible with our interface. Chrome is recommended.
Remember to click the "Submit Scores" button after your evaluation.
3. If you see that videos and the score sliders are not aligned, shrinking your page usually works.
4. If the video seems be stuck, usually waiting for few seconds will sovle this.
If the page not responsive for a long time, please try to refresh it.
6. If you have any questions, please directly contact Thank you for your time and effort!
C.2 More Analysis
P
Figure 13: Hyperparameter analysis of PhysHPO on VideoPhy 6
Analysis of Hyperparameters Figure 13 presents detailed analysis of the impact of various hyperparameters on the performance of PhysHPO on the VideoPhy [6] benchmark. a) The first plot examines the state-level loss weight (A), showing that accuracy improves as À increases, peaking at = 0.4, before declining. This suggests that moderate emphasis on state-level consistency enhances performance, while excessive weighting may lead to overfitting to low-level details. b) Similarly, the motion-level loss weight (p) in the second plot demonstrates a comparable trend, with performance reaching its best p 0.3. This highlights the importance capturing motion dynamics, but also the potential detriment of overemphasizing this aspect. c) The third plot explores the semantic-level loss weight (J)), where accuracy improves with increasing μ up to μ = 0.2, after which the performance plateaus or slightly declines. This underscores the necessity of aligning semantic-level information to ensure coherent contextually accurate video generation. d) Finally, the fourth plot analyzes the balance between error (3er) and gap (8gap) samples in instance-level negative sampling. Results
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indicate that the weighting fge 0.7/0.3) achieves the highest accuracy. as both sample types contribute model robustness. Overemphasizing either error (Ben/ p0p 0.9/0.1) or gap samples (0.1/0.9) diminishes performance, likely due to an imbalanced training signal.
Analysis of Non-preferred Samples The choice of non- non-preferred samples determines their contrast with preferred samples and significantly impacts the model's focus on preference learning. Here, we further explore the selection strategies for non-preferred samples at the instance, state, and motion levels, while ensuring consistency with the corresponding preferred sample configurations in the main text. Specifically, i) Instance-level: The number of generated error videos corresponding to a preferred video, i.e., how many videos are generated to select the error video; ii) State-level: The number of boundary frames swapped to construct non- -oe-frfereedd samples; iii) Motion-level: The choice of structural representations, e.g., depth map, optical flow, and Canny edge.
Claby
8 B
44.93 4558 x.
4559
CLaB 4158
Videolity
VidesPty
Videoiiiy
PlyCeclanch
(4) Instance- Jevel
(b) State-level
(c) Motion-level
Figure 14: Non-preferred samples analysis of (a) Instance-level, (b) State-level, and (c) Motion-level.
Figure 14 illustrates the impact of different non poeffreerrd sample selection strategies on model performance: (a) Instance-level: We observe that selecting three generated samples achieves the best performance. While increasing the number of samples can lead to slight performance gains, also incurs higher computational costs, making three a practical balance. (b) State-level: Swapping a moderate number of boundary frames yields optimal results, as excessive or insufficient frame swapping diminishes the contrast between preferred and non-preferred samples. (c) Motion-level: Optical flow achieves the highest overall score, demonstrating its effectiveness in capturing motion dynamics. In contrast, depth maps and Canny edge representations show relatively lower performance, likely due to their less detailed motion information. These results emphasize the importance of carefully balancing computational efficiency and performance when selecting con-preferred samples. Proper configurations at each level ensure effective and robust model performance.
D Exhibition Board
We provide more comparison results here in Figure 15 focused prompts), along with results on human action/motion in Figure 17 (Hunyuan Video (39] and Figure 18 (CogVideoX (0 We would highly recommend watching the Wehpage.
(with baselines) and Figure 16 (with physically
E Limitation and Future Works
While PhysHPO excels at aligning videos with fine-grained precision, the substantial computational cost required for training large-scale video generation models remains potential limitation, partieu- larly for individuals and organizations with limited resources. Future research should explore more lightweight architectures (e.g., FramePack (96)) or further explore test-time preference alignment to achieve greater performance improvements with reduced computational overhead. Additionally, the data selection strategy introduced in this paper is designed be relatively simple for practicality. Future work should further optimize it based on specific task requirements.
F Ethical Implications
PhysHPO is developed as a hierarchical preference optimization strategy for RESEARCH ONLY. may still raise important ethical considerations, particularly around content generation. The ability to generate high-quality videos can potentially misused for creating misleading or harmful content. To mitigate this risk, we recmmend incorporating safeguards such as adding watermarks to generated videos to ensure transparency and authenticity. Additionally, guidelines on responsible use should be established, emphasizing its application in ethical and creative contexts, such as educational, artistic, or research- based scenarios, while discouraging its use for deceptive or harmful purposes.
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CogVideok
PhyT2v
DPO
PhysHPo
"A whisk egg bowl."
X
PhyT2V
PhysHPO
black pen used on the smooth, white surface of notebook, showcasing the interaction
between the pen and the notebook surface."
CogVideoX
PhyT2V
DPO
PhysHPO
vibrant, elastic beach ball is thrown forcefully towards ground, capturing dynamic interaction with the surface upon impact."
Figure 15: More comparison demonstrations with baselines.
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CogVideoX
PhysHPO
swimmer glides through the calm ocean waves."
CogVideoX
PhysHPO
"A airplane zooms through patch of fluffy clouds."
CogVideoX
PhysHPO
bucket scoops up sea water at the beach."
CogVideoX
PhysHPO
"A French fry dipping into tangy ketchup."
VogVideoX
PhysHPO
"A collection of prisms is arranged in pattern with sunlight shining through them."
CogVideoX
PhysHPO
"Knife slices the tomato."
Figure 16: More results demonstrations with prompts sourced from VideoPhy
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HunyuanVideo
PhysHPO
group friends dancing energetically a party."
HunyuanVideo
PhysHPO
"A person skiing down snowy mountain slope with speed and control."
HunyuanVideo
PhysHPO
person gracefully ice skating on frozen lake."
HunyuanVideo
PhysHPO
"A person doing push-ups in gym with perfect form."
HunyuanVideo
PhysHPO
"A boxer throwing punches and dodging during training session."
HunyuanVideo
PhysHPO
"A runner stretching their legs before starting a race."
Figure 17: More results demonstrations with human action//motion-fccuse prompts.
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CogVideoX
PhysHPO
"A martial artist practicing slow and controlled Tai movements."
CogVideoX
PhysHPO
"A person opening book and flipping through its pages library."
CogVideoX
PhysHPO
runner sprinting through forest trail during sunset."
CogVideoX
PhysHPO
"A skateboarder performing a kickflip on ramp in an urban skatepark."
CogVideoX
PhysHPO
"A person swimming underwater a clear blue pool."
CogVideoX
PhysHPO
"A child flying kite on windy beach."
Figure 18: More results demonstrations with human action/motiion-ocuuse prompts.
33


Reviews:
[{"review_id": "7TZBk9lHbW", "reviewer": "Reviewer_epiP", "summary": "The paper introduces a hierarchical DPO framework that enhances video generation physical plausibility through multi-granularity preference alignment (instance, boundary, motion, semantic). The work also proposes an automated data selection pipeline to select real-world, physically informative, and diverse video-text data from existing large-scale datasets, instead of relying on resource-intensive dataset construction.", "questions": "- Although improvements are shown, the main text lacks systematic analyses of failure cases. where does PhysHPO still fail to produce physically plausible videos, or what are the remaining challenges?\n- Does the filtering pipeline's effectiveness depend heavily on threshold choices and LLM/VLM selection, and if so, how significantly do these parameters impact final model performance?\n- Does the framework maintain its effectiveness for non-physical domains (e.g., stylized content), and are these edge cases systematically evaluated in the experiments?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n- The writing is mostly clear and easy to follow.\n- The four-level hierarchical approach (instance, state, motion, semantic) for DPO is well-motivated and clearly described, addressing core limitations of prior work that only considered coarse alignment. \n- The paper provides comprehensive experimental validation on multiple meaningful, recent benchmarks (VideoPhy, PhyGenBench, VBench) , demonstrating better performance compared to relevant baselines approaches.\n\nWeaknesses:\n\n- Hierarchical modeling may increase training complexity or cost. There is no clear discussion in the main text regarding the computational demands for the hierarchical DPO approach relative to vanilla DPO or SFT approaches. \n- The approach employs LLM/VLM-based scoring and captioning for data selection and semantic preference, but does not thoroughly assess how model choice impacts reliability, e.g., whether different foundation models would yield varying downstream performance.", "was_revised": false, "final_justification": "", "rebuttal": "Thank you to all the reviewers for your thoughtful and constructive comments! We are truly encouraged to see that the reviewers appreciate several positive aspects of our work, such as **strong performance** (Reviewer `epiP`, `EaEb`, `JHEX`, `R9Wj`), **comprehensive & convincing experiments** (Reviewer `epiP`, `R9Wj`), **novel & effective framework** (Reviewer `R9Wj`, `EaEb`), **valuable & practical data selection pipeline** (Reviewer `R9Wj`, `EaEb`, `JHEX`), **well-motivated methodology** (Reviewer `epiP`, `JHEX`), and **clear writing** (Reviewer `epiP`).\n\nYour expertise and thoughtful review have been instrumental in improving our manuscript. Below, we address your comments point-by-point to clarify and strengthen our work.\n\n---\n> `Weakness 1`: Hierarchical modeling may increase training complexity or cost. There is no clear discussion in the main text regarding the computational demands for the hierarchical DPO approach relative to vanilla DPO or SFT approaches.\n\nThank you for pointing out this important aspect. We acknowledge that hierarchical modeling increases computational complexity, but it also brings significant performance gains. Balancing this trade-off presents a challenge and an opportunity for developing more efficient, scalable approaches, which we will also further discuss in future versions.\n\n---\n> `Weakness 2`: The approach employs LLM/VLM-based scoring and captioning for data selection and semantic preference, but does not thoroughly assess how model choice impacts reliability.\n\nThank you for highlighting this issue! Different foundation models indeed impact downstream performance. Here we compare several VLM in our pipeline:\n\n*Table A: Performance Comparison between Different VLMs on VideoPhy*\n||SS|SF|FF|Overall|\n|-|-|-|-|-|\n|w/ LLaMA3-8B|27.5|51.4|48.2|41.0|\n|w/ DeepSeek-V3|29.6|**54.1**|**60.7**|45.1|\n|**w/ Qwen2.5 (Ours)**|**32.4**|**54.1**|58.9|**45.9**|\n\nQwen2.5, a widely used open-source model, outperforms others, emphasizing the importance of choosing suitable LLMs and prompts for task-specific pipelines. Variations in architecture and pretraining data explain these differences, with models like Qwen2.5 excelling in multimodal reasoning tasks.\n\n\n---\n> `Question 1`: Although improvements are shown, the main text lacks systematic analyses of failure cases. where does PhysHPO still fail to produce physically plausible videos, or what are the remaining challenges?\n\nThank you for pointing this out. PhysHPO, as a strategy to enhance the physical plausibility of VDM-generated videos, has demonstrated strong performance and deeper learning from real-world videos. However, a systematic analysis of failure cases reveals certain limitations. Specifically, **high-speed human action videos**, such as **dancing** or **gymnastics**, often fail to capture precise motion dynamics and physical plausibility. This is also largely due to limitations in the **base model**’s ability to represent complex, rapid movements, as well as potential gaps in the **quality** and **quantity** of the training data used.\n\nThese challenges highlight areas for improvement and remain open questions for further exploration by the research community. We recognize the importance of thoroughly analyzing failure cases and will include a more **detailed discussion** in a future version of this work.\n\n---\n> `Question 2`: Does the filtering pipeline's effectiveness depend heavily on threshold choices, and if so, how significantly do these parameters impact final model performance?\n\nYes, the effectiveness of the filtering pipeline does depends on threshold choices, which could impact the quality and relevance of selected data. While `Figure 3` provides a preliminary comparison, here we present a more detailed analysis:\n\n*Table B: Diversity Selection Threshold Analysis on PhyGenBench*\n||Mech.|Opt.|Ther.|Mate.|Overall|\n|-|-|-|-|-|-|\n|0.0|0.50|0.66|0.43|0.60|0.56|\n|0.3|0.53|0.66|0.47|0.60|0.58|\n|0.6|0.53|0.68|0.50|0.63|0.59|\n|**0.9 (Ours)**|**0.55**|**0.68**|**0.50**|**0.65**|**0.61**|\n\nIt clearly demonstrates how selecting appropriate thresholds is crucial for ensuring high-quality data selection and improving downstream task performance.\n\n---\n> `Question 3`: Does the framework maintain its effectiveness for non-physical domains (e.g., stylized content), and are these edge cases systematically evaluated in the experiments?\n\nThank you for your thoughtful question! We have considered this important evaluation criterion and used VBench as the benchmark for non-physical domains, including 16 metrics such as **Appearance Style** (AS), as shown in below:\n\n*Table C: Full Dimensions of VBench Evaluation*\n||Total Score|Quality Score|Semantic Score|SS|BC|TF|MS|DD|AQ|IQ|OC|MO|HA|Color|SR|Scene|AS|TS|OC|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|CogVideoX|81.9|83.1|77.3|96.5|96.7|99.0|97.2|69.5|61.9|63.3|85.1|63.9|98.6|83.0|68.9|52.0|25.0|25.4|27.7|\n|+PhyT2V|82.3|83.3|78.3|96.7|**97.0**|**99.0**|97.3|69.6|62.0|63.5|86.9|64.9|99.2|83.7|71.0|52.3|24.8|26.3|27.7|\n|+Vanilla DPO|82.4|83.3|78.7|96.8|96.8|98.4|97.3|70.2|63.3|63.8|87.2|65.1|99.2|83.9|72.0|53.1|24.9|26.3|27.9|\n|**+PhysHPO (Ours)**|**82.8**|**83.7**|**79.3**|**96.8**|96.9|98.6|**97.5**|**70.3**|**63.5**|**64.8**|**87.4**|**65.4**|**99.5**|**83.9**|**72.2**|**54.1**|**25.7**|**26.5**|**27.9**|\n\nThe results demonstrate that PhysHPO not only significantly enhances the physical plausibility of videos generated by the base model but also maintains or even improves its performance in non-physical (general) domains.\n\n---\nWe hope this rebuttal sufficiently addresses your concerns and clarifies the points raised. We are open to further discussion and greatly appreciate your thoughtful feedback."}, {"review_id": "IjtULHbeEf", "reviewer": "Reviewer_R9Wj", "summary": "This paper introduces PhysHPO, a novel framework for improving the physical plausibility of generated videos. The core contribution is a hierarchical preference optimization method that aligns video generation at four levels: instance, state, motion, and semantic. This fine-grained approach moves beyond existing coarse-grained methods. The paper also proposes a practical and automated data selection pipeline to curate a high-quality, physically relevant dataset from existing large-scale video-text sources, thereby avoiding expensive manual dataset creation. Extensive experiments demonstrate that PhysHPO significantly enhances physical fidelity and overall quality on various benchmarks.", "questions": "1. In your ablation study (Table 3), \"Only w/ L_instance\" outperforms \"Vanilla DPO w/ Our Data\". Your instance-level loss uses a combination of err and gap negative samples. Does the \"Vanilla DPO\" baseline also use this dual-negative sampling strategy, or does it use a more standard approach of generating a single negative sample from the prompt? Clarifying this would help to precisely attribute the performance gains, distinguishing between the improvements from your specific instance-level implementation and the further improvements from the full hierarchical framework.\n\n2. The \"in-depth evolving prompting\" strategy for physical fidelity scoring is a central part of your data selection pipeline. This process seems sensitive to the capabilities of the LLM used (Qwen2.5). Could you provide more insight into the robustness of this selection process?\n\n3. The finding that PhysHPO generalizes better to \"impossible prompts\" is fascinating and suggests a deeper understanding beyond rote memorization of physics. You hypothesize that enhancing physical fidelity equips the model with a stronger ability to generalize. Could you elaborate on this?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths:**\n\n1. The proposed PhysHPO framework is a significant and intuitive contribution to video generation. By decomposing the abstract goal of \"physical plausibility\" into four concrete, hierarchical levels of alignment (Instance, State, Motion, Semantic) , the paper presents a much more sophisticated and fine-grained approach than prior work on Direct Preference Optimization (DPO) in video, which has largely focused on coarse, instance-level alignment. This hierarchical structure is well-justified and provides a clear path to improving specific aspects of video realism.\n\n2. The proposed automated data selection pipeline is a clever and highly valuable contribution. The multi-stage process—filtering for real-world videos , scoring for physical fidelity using an \"in-depth evolving prompting\" strategy with LLMs , and ensuring diversity —is both novel for video generation  and shown to be highly effective\n\n3. The authors have conducted an extensive and convincing set of experiments. They apply their method to multiple advanced, large-scale video models (CogVideoX-2B/5B, Hunyuan Video) and evaluate on a diverse suite of benchmarks, including both physics-focused (VideoPhy, PhyGenBench) and general quality (VBench)  benchmarks. They consistently demonstrate superior performance over strong baselines, including vanilla DPO and test-time optimization methods like PhyT2V.\n\n**Weaknesses:**\n\n1. While the performance improvements shown in the tables are often substantial, the stochastic nature of video generation and automated evaluation metrics means that the robustness of these gains is not statistically validated. \n\n2.  The paper does a good job of trying to isolate variables, for instance by applying vanilla DPO on the same selected data. The ablation in Table 3 shows that \"Only w/ L_instance\" (the authors' instance-level method) outperforms \"Vanilla DPO\". This suggests the instance-level contribution alone provides a boost over the baseline DPO. It would strengthen the paper to more clearly disentangle the gains from the novel instance-level formulation and the gains from the additional hierarchical levels (State, Motion, Semantic).\n\n3. While the paper demonstrates the effectiveness of this approach, it also introduces a dependency on these models. The cost, reproducibility, and potential biases of this \"LLM-as-a-Judge\" paradigm  are not deeply discussed. The selection process is defined by a fixed set of 17 physical phenomena from a prior work, which may limit the breadth of physical concepts the pipeline can identify. A discussion on the sensitivity to the choice of LLM or the physical taxonomy would be beneficial.", "was_revised": true, "final_justification": "After reviewing the author's rebuttal, almost all my concerns have been resolved. Therefore, I would like to keep my original positive score.", "rebuttal": "Thank you to all the reviewers for your thoughtful and constructive comments! We are truly encouraged to see that the reviewers appreciate several positive aspects of our work, such as **strong performance** (Reviewer `epiP`, `EaEb`, `JHEX`, `R9Wj`), **comprehensive & convincing experiments** (Reviewer `epiP`, `R9Wj`), **novel & effective framework** (Reviewer `R9Wj`, `EaEb`), **valuable & practical data selection pipeline** (Reviewer `R9Wj`, `EaEb`, `JHEX`), **well-motivated methodology** (Reviewer `epiP`, `JHEX`), and **clear writing** (Reviewer `epiP`).\n\nWe greatly appreciate your detailed review and valuable insights, which help us refine our manuscript. Here, we respond to your comments systematically to address your concerns.\n\n---\n> `Weakness 1`: While the performance improvements shown in the tables are often substantial, the stochastic nature of video generation and automated evaluation metrics means that the robustness of these gains is not statistically validated.\n\nThank you for your thoughtful question! You are correct that the stochastic nature of video generation and automated evaluation metrics may not fully capture human preferences. This is precisely why presenting sufficient **qualitative results** (`Figure 1`,`4`,`6`,`7`,`15-18`) and conducting **User Studies** (`Figure 5`) are critical to complement quantitative analyses. Additionally, we have evaluated our framework across **multiple** benchmarks (*i.e.*, VideoPhy, PhyGenBench, VBench, IPV-TXT) to minimize potential biases introduced by these limitations.\n\n---\n> `Weakness 2`: It would strengthen the paper to more clearly disentangle the gains from the novel instance-level formulation and the gains from the additional hierarchical levels (State, Motion, Semantic).\n\nThanks for your thoughtful suggestion! `Table 3` progressively removes each Level loss to show their contributions. While our initial version didn’t compare individual Level alignments to vanilla DPO due to time and computational costs, we now provide this ablation:\n\n*Table A: Additional Ablation Study on Level Losses on VideoPhy*\n||SS|SF|FF|Overall|\n|-|-|-|-|-|\n|Vanilla DPO w/ Our Data|28.2|50.0|51.8|41.3|\n|+ $L_\\text{Instance}$|28.9|50.7|51.8|41.9|\n|+ $L_\\text{State}$|29.6|50.0|53.6|42.2|\n|+ $L_\\text{Motion}$|30.3|50.7|55.4|43.0|\n|+ $L_\\text{Semantic}$|28.9|50.0|53.6|41.9|\n\n---\n> `Weakness 3`: (1) The cost, reproducibility, and potential biases of this \"LLM-as-a-Judge\" paradigm are not deeply discussed. (2) The selection process is defined by a fixed set of 17 physical phenomena from a prior work, which may limit the breadth of physical concepts the pipeline can identify. A discussion on the sensitivity to the choice of LLM or the physical taxonomy would be beneficial.\n\n(1) **Dependency on the \"LLM-as-a-Judge\" Paradigm:** Using LLMs significantly improves **efficiency** compared to human annotators, as they can rapidly process video captions and score across 17 dimensions. To address potential biases, we employ an in-depth evolving prompting strategy (`Figure 8`), which guides the LLM through **step-by-step reasoning** and requires it to output both scores and justifications. This reduces hallucinations and ensures **effective** scoring. Furthermore, as shown in `Figures 10` and `11`, our pipeline achieves **over 90% consistency** with human annotations while being cost-effective.\n\n(2) **Scope of the 17 Physical Phenomena:** While LLMs can analyze more granular phenomena, the 17 selected phenomena comprehensively represent core **Dynamic**, **Thermodynamic**, and **Optic** categories. This taxonomy balances breadth and clarity, ensuring interpretability for both LLMs and human annotators without introducing **excessive complexity**. Here, we show a comparison between our 17 phenomena and the categories generated by the LLM itself:\n\n*Table B: Adjacency Accuracy ($\\pm$ 0.3) of Different Scoring Strategies with Human Scoring (Following `Figure 11`'s Setting)*\n||Dynamic|Thermodynamic|Optic|\n|-|-|-|-|\n|LLM Self-Play|72.0|76.0|81.0|\n|Direct Scoring on 3 Categories|74.0|69.0|76.0|\n|**17 Phenomena (Ours)**|**92.0**|**90.0**|**91.0**|\n\n\nThe results demonstrate that our taxonomy effectively captures the most critical aspects while avoiding overly fragmented or ambiguous classifications. We hope this addresses your concerns and welcome further feedback.\n\n---\n> `Question 1`: Does the \"Vanilla DPO\" baseline also use this dual-negative sampling strategy, or does it use a more standard approach of generating a single negative sample from the prompt?\n\nThank you for your detailed review and thoughtful question! **\"Vanilla DPO w/ Our Data\"** uses the standard **one-positive *vs.* one-negative** paradigm, where the positive video is $y_w$ and the negative video is $y_l^{err}$ in our data. The comparison with **\"Only w/ $L_\\text{instance}$\"** highlights the additional contribution of modeling the semantic gap ($y_l^{gap}$) to performance improvements.\n\n---\n> `Question 2`: The \"in-depth evolving prompting\" strategy for physical fidelity scoring is a central part of your data selection pipeline. This process seems sensitive to the capabilities of the LLM used (Qwen2.5). Could you provide more insight into the robustness of this selection process?\n\nThank you for your insightful question! The capabilities of the base model indeed play a significant role in the robustness of the physical fidelity scoring process. As shown in **Table C**, we compare the impact of using different LLMs on PhysHPO’s performance, highlighting the variability introduced by different base models.\n\n*Table C: Performance Comparison between Different LLMs on VideoPhy*\n||SS|SF|FF|Overall|\n|-|-|-|-|-|\n|w/ LLaMA3-8B|27.5|51.4|48.2|41.0|\n|w/ DeepSeek-V3|29.6|**54.1**|**60.7**|45.1|\n|**w/ Qwen2.5 (Ours)**|**32.4**|**54.1**|58.9|**45.9**|\n\nFurthermore, **Table D** demonstrates how both Qwen2.5 and DeepSeek-V3, combined with our in-depth evolving prompting strategy, achieves better alignment with human evaluations. This strategy leverages specific contextual comparisons to guide the model towards more professional and detailed assessments of physical fidelity.\n\n*Table D: Human Align Accuracy with Qwen 2.5 and DeepSeek-V3*\n||Dynamic|Thermodynamic|Optic|\n|-|-|-|-|\n|Qwen2.5|74.0|69.0|76.0|\n|**+ In-Depth Evolving Prompting (Ours)**|**92.0**|**90.0**|**91.0**|\n|DeepSeek-V3|69.0|73.0|71.0|\n|**+ In-Depth Evolving Prompting (Ours)**|**90.0**|**91.0**|**89.0**|\n\n---\n> `Question 3`: The finding that PhysHPO generalizes better to \"impossible prompts\" is fascinating and suggests a deeper understanding beyond rote memorization of physics. You hypothesize that enhancing physical fidelity equips the model with a stronger ability to generalize. Could you elaborate on this?\n\nThank you for your thoughtful question! Our experiments show that PhysHPO generalizes better to \"impossible prompts\". We believe this may stem from **physical fidelity going beyond simple text-pixel alignment**, enabling the model to learn deeper, more abstract relationships between physical and semantic contexts. \n\nBy modeling the boundaries of physics, PhysHPO can creatively explore these limits while maintaining semantic coherence. Its hierarchical alignment strategy further integrates physical understanding with context, allowing logical and consistent generation even in implausible scenarios.\n\nThis suggests that enhancing physical fidelity optimizes the model’s knowledge structure, strengthening its ability to reason and generalize beyond rote memorization.\n\nWe appreciate your interest in this aspect of our work!\n\n---\nThank you again for your valuable feedback. We hope this rebuttal resolves your concerns and enhances the clarity of our work. We remain open to any further suggestions for improvement."}, {"review_id": "qMDgYbE26z", "reviewer": "Reviewer_EaEb", "summary": "The paper presents an approach named PhysHPO for physically plausible video generation. It presents a cross-modal (text-video), direct preference optimization framework to enable the physical plausibility in video generation. It ensures the video alignment of the text prompt and a physically plausible video on four granular/fine-grained levels - instance, state, motion, and semantic. Additionally, it proposes an automated data selection pipeline based on reality, physical fidelity, and diversity to curate real-world video data that shows physical laws (dynamic objects, thermodynamic, and optics) and the dataset is diverse as well. This diverse and curated dataset is then used for training via direct preference optimization on the four level of granularity. The method is evaluated on physics-based benchmarks VideoPhy, PhyGenBench and VBench and shows significant improvements. \n\nThe core contributions of the work are: 1) compared to the previous work that explore only instance level alignment, this work explores a more fine-grained approach to ensure alignment, 2) The work presents an automated approach to curate a diverse dataset of video showcasing physical laws which removes the efforts of curating own data from scratch.", "questions": "Questions:\nWhile the metrics used in the paper are inspired from the physics-based benchmarks, can the authors discuss how and why these metrics are good enough to evaluate the physics of the video? To my understanding, they are heavily based on human evaluation and motion accuracy, but I am unclear if they can efficiently evaluate the accuracy of the physics in the video.\n\nSuggestions:\n1. The metrics used in the paper can be explained in more detail. Some lines from the supplementary can be moved to the main paper to explain about the metrics used.", "limitations": "Yes. Section F of the supplementary material addresses the ethical implications clearly.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. The videos in the supplementary show improvement when compared with CogVideoX and show that the method is learning physical plausibility.\n2. The contribution of presenting an automated method to curate a diverse video dataset having physical laws of dynamic objects, optics, and thermodynamics adds strength to the work.\n\nWeakness:\nThe work uses four levels of granularity - instance, state, motion, and semantic. The semantic loss ensures that there is alignment between the text prompt and the video while the motion loss ensures the video's physical motion. For prompts such as 'a ceramic cup vanishes into thin air' in Figure 6, there can be a possibility that the model tries to over-optimize the semantic loss to ensure the alignment between the text prompt and video, finds a shortcut/cheat and doesn't actually optimize the motion loss much (cheats the motion loss objective). In my opinion, the motion loss is quite important to ensure the physical motion accuracy. I am curious to know how the proposed method ensures that the semantic loss doesn't become the primary/dominating loss function and finds a sub-optimal solution, and how the method ensures that the motion loss is also penalizing the network (given the loss weights are almost equal, 0.3 for motion and 0.2 for semantic).", "was_revised": true, "final_justification": "During the rebuttal phase, the authors clarified all my concerns. I would thus like to maintain my positive score.", "rebuttal": "Thank you to all the reviewers for your thoughtful and constructive comments! We are truly encouraged to see that the reviewers appreciate several positive aspects of our work, such as **strong performance** (Reviewer `epiP`, `EaEb`, `JHEX`, `R9Wj`), **comprehensive & convincing experiments** (Reviewer `epiP`, `R9Wj`), **novel & effective framework** (Reviewer `R9Wj`, `EaEb`), **valuable & practical data selection pipeline** (Reviewer `R9Wj`, `EaEb`, `JHEX`), **well-motivated methodology** (Reviewer `epiP`, `JHEX`), and **clear writing** (Reviewer `epiP`).\n\nThank you for your insightful feedback and expertise, which have guided us in improving the clarity and quality of our paper. Below, we provide detailed responses to your comments.\n\n---\n> `Weakness`: How does the method ensure that the semantic loss doesn’t dominate and lead to sub-optimal solutions, while the motion loss effectively penalizes the network, given their similar weights (0.3 for motion and 0.2 for semantic)?\n\nThank you for raising this important concern! We acknowledge that Motion Loss and Semantic Loss are optimized simultaneously during training, which could theoretically lead to the model overfitting to Semantic Loss and neglecting Motion Loss. Below, we provide a detailed explanation of how our framework mitigates this issue:\n\n- **Independent Loss Design:** Motion Loss and Semantic Loss operate on distinct inputs, where Motion Loss targets video structural information (*i.e.*, optical flow), while Semantic Loss focuses on text-video alignment. The physical characteristics captured by Motion Loss could not be \"cheated\" by optimizing Semantic Loss.\n- **Positive-Negative Sample Design:** Both losses rely on strict positive-negative sample comparisons. Even if Semantic Loss is over-optimized, Motion Loss penalizes discrepancies in physical motion between positive and negative samples, ensuring motion accuracy remains a priority.\n- **Experimental Validation:** Here we conduct an ablation on impossible prompts (IPV-TXT in `Figure 5`) to evaluate the impact of Motion Loss and Semantic Loss individually. \n\n    *Table A: Robustness Testing on IPV-TXT*\n    ||Visual Quality|Prompt Following|\n    |-|-|-|\n    |**PhysHPO (Ours)**|**58.82**|**47.09**|\n    |w/o $L_\\text{Motion}$|54.06|44.93|\n    |w/o $L_\\text{Semantic}$|55.39|43.75|\n    |Baseline|36.54|30.12|\n\n    Results show that removing either loss significantly reduces the model’s ability to generate impossible videos. Both are critical for handling complex prompts, demonstrating their complementary roles in the optimization process.\n\nOverall, the framework is designed to ensure that Motion Loss remains critical for physical motion accuracy while Semantic Loss complements it without dominating the optimization process. We appreciate your thoughtful feedback and hope this clarifies our approach!\n\n---\n> `Question`: Can the authors discuss how and why these metrics are good enough to evaluate the physics of the video? To my understanding, they are heavily based on human evaluation and motion accuracy, but I am unclear if they can efficiently evaluate the accuracy of the physics in the video.\n\nThank you for your thoughtful question! Our work utilizes **Semantic Adherence (SA)** and **Physical Commonsense (PC)** as core metrics to evaluate the physics of the generated videos, which are also widely adopted in recent works [1-3]. PC goes beyond simple motion accuracy by assessing adherence to real-world physical laws and intuitive commonsense, such as conservation of mass or gravity, while SA ensures alignment between the video content and the textual description. Together, these metrics provide a comprehensive framework for evaluating physical plausibility and semantic accuracy.\n\nImportantly, our evaluation uses VideoCon-Physics from VideoPhy for **automated** assessment, ensuring **scalability** and **objectivity**. However, we acknowledge that automated metrics have certain limitations, such as challenges in fully aligning with human preferences. To address this, benchmarks often **separate different physical phenomena for evaluation** to improve accuracy. Additionally, we emphasize the importance of **qualitative results** (`Figure 1`,`4`,`6`,`7`,`15-18`) and **user studies** (`Figure 5`) to complement automated metrics. To mitigate potential biases, we also perform quantitative tests across **multiple** benchmarks (*i.e.*, VideoPhy, PhyGenBench, VBench, IPV-TXT).\n\nWe hope this clarifies how our metrics are designed and their role in evaluating the physics of generated videos!\n\n---\n> `Suggestion`: The metrics used in the paper can be explained in more detail. Some lines from the supplementary can be moved to the main paper to explain about the metrics used.\n\nThank you for the suggestion! We will move key details about SA and PC from the supplementary to the main paper for better clarity in our revision.\n\nWe appreciate your insightful comments again and hope this rebuttal addresses your questions. Please feel free to reach out with further suggestions to improve clarity.\n\n---\n[1] PhyT2V: LLM-Guided Iterative Self-Refinement for Physics-Grounded Text-to-Video Generation [CVPR’25]\n\n[2] Articulated Kinematics Distillation from Video Diffusion Models [CVPR’25]\n\n[3] VLIPP: Towards Physically Plausible Video Generation with Vision and Language Informed Physical Prior [ICCV’25]"}, {"review_id": "5VLbQmRqw7", "reviewer": "Reviewer_JHEX", "summary": "The paper introduces PhysHPO, a framework for improving the physics of T2V model using a fine-grained preference optimization approach. PhysHPO includes preference pairs at different aspects, including instance-level, state-level, motion-level and semantic-level. The work also includes a data selection pipeline with the aim to select a subset of videos from a large set (~0.4M) that optimizes the fine-tuning performance. The proposed PhysHPO seems different from general DPO setting as it creates win-lose pairs by intervening the generation process of the losing video to make it worse. Experimental results show that PhysHPO outperforms a few baselines including SFT, DPO and PhyT2V.", "questions": "See weaknesses", "limitations": "See weaknesses", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "(+) The paper addresses fine-grained DPO for video generation, which is an important direction. The framework proposes DPO design from four different perspectives and use their ensemble to enhance physics in the generated videos. \n\n(+) PhysHPO is essentially an annotation-free method that doesn't require human preference data. The preference label comes from either intervening the ground truth video to create a deteriorate one or by manipulating the input condition and generate a deteriorate one with the T2V model. \n\n(+) Experiment results shows obvious improvements from PhysHPO compared to other baselines.\n\n(-) I am not sure if this PhysHPO is actually a DPO method because it has a different setting from the original definition of DPO. There should be a paragraph that explicitly clarifies the difference between PhysHPO and DPO. I am pretty confused after reading the whole paper and if I understand correctly, PhysHPO has a different definition of p_ref() because this p_ref() is essentially the ground truth video which is assumed to always win the comparison. \n\n(-) As suggested above, the whole writing need some improvement to make things clear. The data selection section has a very loose connection with the actual methodology section unless one could realize in advance that this is not a standard DPO method. \n\n(-) It is not very clear to me why PhysHPO would work since it always assume the ground truth video wins the comparison and the losing video is generated by manipulating the generation process. Some of the operation seems pretty heuristic like replacing the start and ending frames with irrelevant ones (state-level), masking the prompt tokens (instance-level), rephrasing the input prompts (semantic-level).\n\n(-) A lot the details need to be explained: what is u_motion in line 248? How is y_w actually used since it's a ground truth video instead of one being generated by the reference model? Did you add noise to it and then input the noisy ground truth video to reference model and obtain the denoising outputs as the signal for loss calculation? I feel like the equations need to be write explicitly like in the original diffusion DPO paper (e.g. eq 14). Perhaps I have misunderstood something from the paper so please clarify. 3) Since human preference annotation were not collected, how did you implemented vanilla DPO in table 2? Is the winning sample still assumed to be the ground truth video? What are the losing samples and how did you get them?", "was_revised": true, "final_justification": "The authors have addressed most of my concerns. Please work on the writing to resolve the confusion in notations.", "rebuttal": "Thank you to all the reviewers for your thoughtful and constructive comments! We are truly encouraged to see that the reviewers appreciate several positive aspects of our work, such as **strong performance** (Reviewer `epiP`, `EaEb`, `JHEX`, `R9Wj`), **comprehensive & convincing experiments** (Reviewer `epiP`, `R9Wj`), **novel & effective framework** (Reviewer `R9Wj`, `EaEb`), **valuable & practical data selection pipeline** (Reviewer `R9Wj`, `EaEb`, `JHEX`), **well-motivated methodology** (Reviewer `epiP`, `JHEX`), and **clear writing** (Reviewer `epiP`).\n\nYour thoughtful review has been invaluable in helping us enhance our manuscript. Here, we address your comments thoroughly to ensure clarity and accuracy.\n\n---\n> `Weakness 1`: How does PhysHPO align with the original definition of DPO, given its different setting? Specifically, how is $p_{ref}()$, defined as the ground truth video that always wins comparisons, consistent with DPO?\n\nThank you for raising this important point! We would like to respectfully clarify that PhysHPO indeed belongs to the DPO paradigm. In PhysHPO, $p_\\theta$ represents the policy video diffusion model, and $p_{ref}$ denotes the reference model, as outlined in the `Preliminaries` section of the paper. The key modification in PhysHPO lies in constructing **new** positive and negative sample pairs to explore physical commonsense. This does **not** alter the standard selection process between $p_\\theta$ and $p_{ref}$, ensuring PhysHPO remains consistent with the core DPO methodology. \n\n---\n> `Weakness 2`: How can the writing be improved to better connect the data selection section with the methodology, especially given that this is not a standard DPO method?\n\nThank you for the feedback! We will revise the writing to better clarify how the data selection ties into the methodology and explicitly highlight the connection to PhysHPO as a DPO adaptation. This will improve the flow and make the methodology clearer.\n\n---\n> `Weakness 3`: Why does PhysHPO work when it assumes the ground truth video always wins and the losing video is generated through heuristic manipulations, such as replacing frames (state-level), masking prompt tokens (instance-level), or rephrasing prompts (semantic-level)?\n\nThank you for your comment! The design of PhysHPO assumes that the ground truth video always wins the comparison, while losing videos are generated by introducing physical or semantic inconsistencies. Operations at instance, state, and semantic level are designed to **simulate common errors** in physical reasoning or semantic alignment. Specifically, \n\n- **State-level** (*e.g.*, replacing frames) disrupts physical continuity, mimicking unrealistic state transitions such as sudden object disappearance or appearance.\n- **Instance-level** (*e.g.*, masking prompt tokens) introduces semantic deviations, helping the model learn the relationship between the prompt and the video.\n- **Semantic-level** (*e.g.*, rephrasing prompts) creates subtle semantic inconsistencies, further challenging the model to align video generation with the intended meaning.\n\nAlthough these designs may appear heuristic, they are grounded in physical commonsense and semantic reasoning principles. \n\nAdditionally, beyond the ablation studies in `Table 3`, which remove each level alignment progressively, we conducted further ablation experiments focusing on the contribution of each level:\n\n*Table A: Additional Ablation Study on Level Losses on VideoPhy*\n||SS|SF|FF|Overall|\n|-|-|-|-|-|\n|Vanilla DPO w/ Our Data|28.2|50.0|51.8|41.3|\n|+ $L_\\text{Instance}$|28.9|50.7|51.8|41.9|\n|+ $L_\\text{State}$|29.6|50.0|53.6|42.2|\n|+ $L_\\text{Motion}$|30.3|50.7|55.4|43.0|\n|+ $L_\\text{Semantic}$|28.9|50.0|53.6|41.9|\n\nThe results show that every level of preference alignment contributes to improving the physical plausibility of video generation. We hope this further clarifies our rationale.\n\n\n---\n> `Weakness 4`: A lot the details need to be explained: **(1)** What is $u_{motion}$ in line 248? **(2)** How is $y_w$ (ground truth video) used, given it isn’t generated by the reference model? Did you add noise and use denoising outputs for loss calculation? **(3)** Should the equations be explicitly written, like in the original diffusion DPO paper (*e.g.*, eq. 14)? **(4)** Without human preference annotations, how was Vanilla DPO implemented in Table 2? Was the winning sample assumed to be the ground truth video, and how were the losing samples generated?\n\nThank you for your detailed questions and suggestions, and we apologize for any potential confusion caused!\n\n1. $u_{motion}$ refers to the motion-level loss calculation function for positive and negative video structure pairs.\n\n2. Regarding $y_w$, it is indeed the ground truth video, but we **add noise** to it before inputting it into the reference model. This approach aligns with prior works like VideoDPO[1], where winning videos are **pre-generated** by the reference model and then used as training data, rather than being directly generated during training.\n\n3. We appreciate your suggestion on improving the clarity of the equations and will revise the formulas in future versions to ensure they are more explicit.\n\n4. For Vanilla DPO in `Table 2`, we implemented it **using our dataset** (*i.e.*, `\"Vanilla DPO w/ Our Data\"`) for fair comparison. In this setup, the winning sample corresponds to $y_w$ (ground truth video), while the losing sample corresponds to $y_l^{err}$, which is generated using the caption associated with $y_w$ as the prompt.\n\n---\nThank you again for your detailed feedback. We hope this rebuttal clarifies the points raised and addresses your concerns. We are happy to continue discussing ways to improve the paper.\n\n---\n[1] VideoDPO: Omni-Preference Alignment for Video Diffusion Generation [CVPR’25]"}]

Author rebuttal:

Meta-review: This paper introduces PhysHPO, a hierarchical direct preference optimization framework that improves physical plausibility in video generation. 

All four reviewers gave positive ratings, recognizing the paper's strong performance, comprehensive experiments, novel hierarchical framework, and valuable automated data selection pipeline. The reviewers particularly appreciated the well-motivated methodology that decomposes physical plausibility into four hierarchical levels (instance, state, motion, semantic) and the practical contribution of an automated data curation process that eliminates the need for expensive manual dataset construction.

The main concerns raised during the initial review phase centered on computational complexity, model choice sensitivity in the data selection pipeline, failure case analysis, the balance between different loss components, and clarity of mathematical formulations. The authors provided a thorough rebuttal addressing each of these points with additional ablation studies, comparisons across different language models, detailed explanations of loss design, and clarifications on implementation details.

Following the rebuttal, reviewers R9Wj and EaEb explicitly acknowledged that their concerns had been resolved and confirmed their positive ratings. While reviewers epiP and JHEX did not provide final confirmations, reviewer JHEX's final justification stated that "the authors have addressed most of my concerns" and recommended working on writing clarity, indicating general satisfaction with the responses.

The experimental evaluation is comprehensive, covering multiple advanced video models and diverse benchmarks including both physics-focused (VideoPhy, PhyGenBench) and general quality (VBench) evaluations. The consistent improvements over strong baselines, including vanilla DPO and specialized methods like PhyT2V, demonstrate the effectiveness of the approach.
Decision: Accept (poster)