PhysMaster: Mastering Physical Representation for Video Generation via Reinforcement Learning

## Abstract

Abstract Video generation models nowadays are capable of generating visually realistic videos, but often fail to adhere to physical laws, limiting their ability to generate physically plausible videos and serve as “world models”.
To address this issue, we propose PhysMaster, which captures physical knowledge as a representation for guiding video generation models to enhance their physics-awareness.
Specifically, PhysMaster is based on the image-to-video task where the model is expected to
predict physically plausible dynamics from the input image.
Since the input image provides physical priors like positions, materials, and interactions of objects in the scenario, we devise PhysEncoder to encode such physical representation as an extra condition.
Nevertheless, there is no well-established definition for a physical representation. Thus, we could not use an off-the-shelf model to extract the physical condition, or set a straightforward supervision to train PhysEncoder.
To solve this challenge, we adopt a top-down optimization strategy, where the PhysEncoder is optimized based on the physical plausibility of the final generated videos using reinforcement learning (RL).
Through this top-down optimization, the PhysEncoder can effectively capture implicit physical cues from the starting image and inject them into the video generation process.
Experiment results prove that, our method significantly enhances the model’s physics-awareness as a plug-in, and demonstrate strong performance on both specialized proxy tasks and general open-world scenarios.

## 1 Introduction

Video generation models (Brooks et al., 2024; Kuaishou, 2024; Yang et al., 2024c) have developed rapidly nowadays, achieving significant performances in generating
visually appealing videos (RunwayML, 2024; Team, 2024; Kong et al., 2024).
However, they primarily act as sophisticated pixel predictors based on case-specific imitation, and often face challenges in adherence to physical laws (Kang et al., 2024; Liu et al., 2025a; Meng et al., 2025).
This limits their ability to generate physically plausible videos and
further comprehend physical principles to serve as “world models”.
To evolve these models from content creators to world simulators, we aim to
incorporate physical knowledge into the video generation process to enhance their physical realism.

We summarize the specific challenges of physics-aware video generation into the following two points. First, the commonly used Mean Squared Error (MSE) loss for data-driven finetuning focuses on appearance fitting rather than comprehension of physical knowledge, and it is non-trivial to directly supervise the physical performance of pretrained models beyond merely appearance.
Second, generative models struggle to extract appropriate physical knowledge from a textual instruction or an input image and translate it into physical guidance for generation, which demands logical reasoning from descriptions or images to physical knowledge, and to visual phenomena. Progress for enhancing physics-awareness of video generation has been made and the solutions can be broadly categorized into two types based on the usage of simulation. Simulation-based approaches (Lv et al., 2024; Liu et al., 2024b) attempt to apply physics-based simulation results to guide video generation, but they are often
constrained in the range of simulable physical processes and modalities, lacking the potential to generalize to diverse phenomena.
Simulation-free methods (Xue et al., 2024; Furuta et al., 2024) rely on post-training on physics-rich data or employ reinforcement learning for aligning to human preference. The former highly depends on fitting similar training samples, and the latter utilizes either expensive human annotation suffering from rater variability, or scalable but inaccurate AI evaluators.
In summary, existing works find it hard to truly abstract and understand physics of the world (Lin et al., 2025; Motamed et al., 2025), hindering generalization to diverse physics.

Facing the aforementioned challenges, we propose to learn a physical representation as the bridge between the necessary physical knowledge and generated videos to guide generative models towards physics awareness.
Specifically, we focus on image-to-video (I2V) generation where an initial frame and textual description are given, the model is supposed to predict physically plausible dynamics from input scenes.
The input image offers visual cues like object configurations, relative positions, and potential interactions that largely dictate the subsequent physical evolution of video, making it
a reliable source of physical priors. Thus firstly, we devise a physical encoder, PhysEncoder, to extract implicit physical representation from the input image as an extra input condition to guide the generation process for enhancing physics-awareness of the model.

However, how to learn an effective physical representation for video generation remains an open question. Without a well-established definition for physical representation, we can not conduct straightforward supervision of PhysEncoder for training. Thus we propose a top-down optimization strategy to optimize its ability for guiding physically plausible video generation by reinforcement learning with human feedback (RLHF) framework, which has proven its effectiveness in finetuning of both large language models (LLMs) (Yuan et al., 2023; Xu et al., 2024; Yuan et al., 2023) and generative models (Lee et al., 2023; Prabhudesai et al., 2024; Fan et al., 2023).
The physical plausibility of generated videos guided by PhysEncoder acts as feedback for optimizing PhysEncoder to extract effective physical representations.
Specifically, we train PhysEncoder on human preference data via Direct Preference Optimization (DPO) (Rafailov et al., 2023) in a three-stage training pipeline. We first conduct supervised fine-tuning (SFT) of both base model and PhysEncoder, then we adopt a two-stage DPO
with pairwise supervision based on the physical plausibility of the final generated
videos to enhance PhysEncoder’s capacity to capture physical representations and model’s physical performance, with trainable module separately set as
LoRA (Hu et al., 2021) of the DiT model and PhysEncoder. With SFT providing the model with initial ability to predict
physically plausible videos under the guidance of simultaneously finetuned PhysEncoder, the subsequent DPO processes further enable PhysEncoder to effectively capture implicit physical cues
from the starting image and inject them into video generation, thus helping improve physical understanding of the model.

Last but not least, we condition the video generation model on physical representation in a plug-in manner, enhancing physics-awareness by injecting physical knowledge into it. Such a paradigm enables the model to learn general physical properties, rather than overfitting to specific phenomena or being constrained to particular motion modalities as in previous works, thus allowing it to generalize to diverse scenarios.
We demonstrate the effectiveness of PhysEncoder starting from a specialized proxy task of “free-fall”, and then generalize to general open-world physical scenarios governed by a wide range of physical laws. PhysEncoder proves its capablity by guiding the generation model towards enhanced physical performance, and such generalization implies that PhysMaster, our representation learning paradigm facilitates the physical comprehension in a broad scope.

In summary, PhysMaster provides a more generalizable solution for video generation models to capture physical knowledge across diverse physical phenomena,
showing its advantage in acting as a foundational solution for physics-aware video generation and potential to energize more fancy applications (Agarwal et al., 2025; Yang et al., 2024b; 2023).

## 2 Related Works

Physics-aware video generation.
While recent video generation models achieve impressive visual effects (Brooks et al., 2024; Kong et al., 2024), they still struggle with adherence to real-world physical laws (Lin et al., 2025; Kang et al., 2024).
Physics-aware video generation approaches can be broadly categorized based on the application of explicit physical simulation. Simulation-based methods (Lv et al., 2024; Xie et al., 2025; Montanaro et al., 2024; Zhang et al., 2024b) guide generation with
simulation results.
PhysGen (Liu et al., 2024b) utilizes rigid-body dynamics simulated with physical parameters inferred by large foundation models. PhysMotion (Tan et al., 2024) relies on MPM-based simulation to generate coarse videos which are refined by a video diffusion model.
As for simulation-free approaches, they either fine-tune on large-scale video datasets to implicitly internalize physical priors (Wang et al., 2025; Zhang et al., 2025), or use reinforcement learning with feedback from human annotators or vision-language models (Xue et al., 2024; Furuta et al., 2024).
PhyT2V (Xue et al., 2024) uses MLLMs to refine prompts iteratively through multiple rounds of generation and reasoning.
WISA (Wang et al., 2025) incorporates structured physical information into the generative model and uses Mixture-of-Experts for different physics categories.
However, those methods are restricted to fixed physical categories or exhibit limited physical comprehension. Our PhysMaster incorporates physical knowledge into video generation process via physical representation to enhance general physics-awareness.

RLHF for video generation.
Inspired by the success of RLHF in LLMs (Ouyang et al., 2022; Jaech et al., 2024), researchers have explored applying this paradigm to video generation (Zhang et al., 2024a; Qian et al., 2025). VideoDPO (Liu et al., 2024a) pioneers the adaptation of DPO (Rafailov et al., 2023) to video diffusion models by considering both visual quality and semantic alignment for data pair construction. VideoAlign (Liu et al., 2025b) introduces a multi-dimensional video reward model and DPO for flow-based video generation model based on it.
PISA (Li et al., 2025) investigates specifically for video generation of object free-fall, improving physical accuracy through reward modeling based on depth and optical flow.
Unlike the aforementioned methods, we optimize a physical encoder rather than the whole video generation model by leveraging generative feedback from the model’s outputs.
This paradigm mitigates overfitting to specific physical processes and promotes the encoder’s generalizability for learning universal physical knowledge through RLHF.

## 3 Method

Based on I2V setting, PhysMaster extracts physical representation from the input image and optimizes both the generation model and PhysEncoder in a three-stage training pipeline.
It seeks direct supervision from groundtruth via SFT and pairwise supervision from generated videos via DPO, and is implemented on the specialized proxy task and general open-world scenarios.
We will separately detail physical representation (Sec [3.1](https://arxiv.org/html/2510.13809v1#S3.SS1)), task formulation (Sec [3.2](https://arxiv.org/html/2510.13809v1#S3.SS2)) and training scheme (Sec [3.3](https://arxiv.org/html/2510.13809v1#S3.SS3)).

Figure: Figure 1: Overall architecture of PhysMaster. Given an input image, PhysEncoder encodes its physical feature and concatenates with visual features, then the DiT model predicts subsequent frames conditioned on physical, visual, and text embeddings. We optimize PhysEncoder ’s physical representation via feedback from generated video pairs of the model by maximizing reward derived from “positive” and “negative” video outputs in a DPO paradigm.
Refer to caption: x1.png

### 3.1 Physical Representation

PhysMaster is implemented upon a transformer-based diffusion model (DiT) (Peebles & Xie, 2023), which employs 3D Variational Autoencoder (VAE) (Kingma, 2013)
to transform videos and initial frame to latent space, and T5 encoder $\mathcal{E}_{T5}$ (Raffel et al., 2020) for text embeddings $c_{text}$.

We propose to learn a physical representation from input image as extra guidance for the I2V model to inject physical information, since the input image contains not only explicit physical states, such as object material and spatial distribution, but also implicit physical laws, like the gravitational field.
It is worth expecting that the learned physical representation can be used as a generalizable guidance of both physical properties and dynamics for physics-aware video generation.
Following the structure of Depth Anything (Yang et al., 2024a), we build PhysEncoder with a DINOv2 (Oquab et al., 2023) encoder and a physical head. The former adopts pretrained weights from Yang et al. (2024a) for initialization and
takes the role of semantic perception, while the latter adapts the extracted high-level semantic features into an appropriate dimension to be injected into the DiT model.
Taking the first frame as image input, PhysEncoder encodes it into physical embeddings $c_{phys}$, which are then fed into DiT model after concatenated with image embeddings $c_{image}$.
For SFT, the flow-based DiT model with weights ${\theta}$ directly parameterizes the $v_{\theta}(z_{t},t,c_{text},c_{image},c_{phys})$ to regress velocity $(z_{1}-z_{0})$ with the Flow Matching objective (Lipman et al., 2022):

$$ $\mathcal{L}_{LCM}=\mathbb{E}_{t,z_{0},\epsilon}||v_{\theta}(z_{t},t,c_{text},c_{image},c_{phys})-(z_{1}-z_{0})||_{2}^{2}.$ (1) $$

### 3.2 Task Formulation

Our work aims to provide a scalable and generalizable methodology for learning physics from targeted data, so for demonstrating the effectiveness of our PhysMaster, we start by defining a proxy task under specialized physical principles and construct domain-specific data for preliminary validation; then we verify its generalizability across a broader range of physical laws and various tasks.

Proxy task. For preliminary verification, “free-fall” (involving the complete physical process of objects dropping from mid-air and colliding with other objects on a surface), a specialized yet expressive scenario is chosen as the proxy task for the following characteristics. First, “free-fall” embodies clear and fundamental physical principles (e.g., energy and momentum conservation) shared across diverse physical scenarios, making it a suitable representative for further generalization. Second, such physical scenario involves a wide range of object-level physical properties, such as density, elasticity, and hardness, allowing proof of generalizability of learned representations across different physical attributes. Third, this task can be easily simulated for scalable generation of synthetic data and allows for straightforward evaluation by comparing generated videos against ground-truths. The reason is that by assuming the falling object starts from rest and is only influenced by gravity, the trajectories of objects become fully deterministic given the initial frame, which also enables automatic construction of preference video pairs for DPO by similarity with ground-truths.

General open-world scenarios. We further substantiate generalization capabilities of PhysMaster across diverse physical processes. Following WISA (Wang et al., 2025), we include large-scale scenarios broadly covering common physical phenomena observed in real world for PhysEncoder to acquire a far more comprehensive and generalizable understanding of physical laws and thus effectively enhances the physics awareness of the video generation model. Different from the proxy task implementation, we modify the text prompts provided to the generation model by adding domain-specific prefixes (e.g., “Optic, A ray of light …”, “Thermodynamic, A glass of water …”). This conditions the model on the type of involved physics laws and guides it to associate visual phenomena with underlying physics extracted from PhysEncoder. For preference assignment, we rely on human annotators to provide pairwise labels for DPO data construction and evaluation.

### 3.3 Training Scheme

We propose a three-stage training pipeline for PhysMaster to enable physical representation learning of PhysEncoder by leveraging the generative feedback from I2V model. The core idea is formulating DPO for PhysEncoder with the reward signal from generated videos of pretrained DiT model,
thus help physical knowledge learning without explicit modeling.

Stage I: SFT for DiT and PhysEncoder. First, we condition the I2V base model on physical representation from PhysEncoder by SFT, thus it is possible for us
to optimize PhysEncoder with the performance of model as feedback in following stages. Since PhysEncoder’s training starts from the frozen DINOv2 with pretrained weights from Depth Anything (Yang et al., 2024a) and trainable physical head with randomly initialized weights, this stage can be viewed as adapting Depth Anything for physical condition injection.
As in Figure [1](https://arxiv.org/html/2510.13809v1#S3.F1), by concatenating physical embeddings extracted by PhysEncoder with visual embeddings encoded by VAE,
we inject physical representation as extra condition to the model.
SFT following Eq [1](https://arxiv.org/html/2510.13809v1#S3.E1) equips the model with the initial
ability to predict subsequent frames from the input image, guided by simultaneously finetuned PhysEncoder.

Stage II: DPO for DiT.
Second, we expect to adapt the output of the pretrained model to a more physically plausible distribution, paving the
way for the PhysEncoder to learn from generated videos with higher physical accuracy.
Then in Stage II, we apply LoRA (Hu et al., 2021) to finetune the DiT model on preference dataset with DPO, during which the model learns to generate positive samples with higher probability and negative samples with lower probability.
Regarding I2V setting, each sample in our preference dataset includes a prompt $p$, an image $i$, a human-chosen video
$x^{w}$ and a human-rejected video $x^{l}$.
The goal of DPO is to learn a conditional distribution $\pi_{\theta}(x\mid p,i)$ that maximizes
the reward $r_{\phi}(x,p,i)$ while staying close to a reference model $\pi_{\text{ref}}$:

$$ $\displaystyle\max_{\pi_{\theta}}\$ $\displaystyle\mathbb{E}_{p\sim\mathcal{D}_{p},i\sim\mathcal{D}_{i},x\sim\pi_{\theta}(x\mid p,i)}\left[r_{\phi}(x,p,i)\right]-\beta\,\mathbb{D}_{\text{KL}}\left[\pi_{\theta}(x\mid p,i)\,\|\,\pi_{\text{ref}}(x\mid p,i)\right]$ (2) $$

where
$\beta$ controls the regularization term (KL-divergence) from $\pi_{\text{ref}}$.
For our flow-based DiT model, Flow-DPO objective (Liu et al., 2025b) $L_{\text{FD}}(\theta)$ is then given by:

$$ $\displaystyle-\mathbb{E}\Bigg[\log\sigma\Bigg($ $\displaystyle-\frac{\beta}{2}\Big(\|v^{w}-v_{\theta}(x_{t}^{w},t)\|^{2}-\|v^{w}-v_{\text{ref}}(x_{t}^{w},t)\|^{2}-\bigl(\|v^{l}-v_{\theta}(x_{t}^{l},t)\|^{2}-\|v^{l}-v_{\text{ref}}(x_{t}^{l},t)\|^{2}\bigr)\Big)\Bigg)\Bigg],$ (3) $$

where the conditioning prompt $p$ and image $i$ are omitted for simplicity, $v_{\theta}$ denotes the predicted velocity field, $v^{w}$, $v^{l}$ are the target velocity of “preferred” and “less preferred” data.

The pretrained DiT model from Stage I is regarded as the reference model and is used to construct the preference data pairs. Specifically, we generate two groups of videos with the pretrained model using the same prompt $p$ and initial frame $i$ but different seeds.
By establishing clear distinctions between positive samples $x^{w}$ and negative samples $x^{l}$, the model learns to generate physically plausible
videos. As a result, we further enhance the model’s physics awareness.

Stage III: DPO for PhysEncoder. We leverage generative feedback from the pretrained DiT model to optimize PhysEncoder’s physical representation via DPO paradigm.
As illustrated in Figure [1](https://arxiv.org/html/2510.13809v1#S3.F1), our framework consists of two parts: PhysEncoder to be optimized and the pretrained DiT model providing generative feedback.
With physical head of PhysEncoder the only trainable module, Stage III shares the same training objective Eq [3](https://arxiv.org/html/2510.13809v1#S3.E3) with Stage II, differing solely in the learnable parameters.
$L_{\text{FD}}(\theta)$ leads PhysEncoder to learn a physical representation that
guides the predicted velocity field $v_{\theta}$ closer to the target velocity $v^{w}$ of the “preferred” data.
In this manner, by directing the DiT
model to generate more accurate physical dynamics, the PhysEncoder’s original representation is gradually
optimized with more physical knowledge through
model feedback.

## 4 Experiments

To evaluate the effectiveness of PhysMaster for physical representation learning
and demonstrate its potential to enhance physical performance of the DiT model, comprehensive experiments are conducted
on both the proxy task and wide-ranging scenarios.

### 4.1 Implentation Details

Training configuration. The training of both PhysEncoder and the DiT model is conducted on 8 NVIDIA-A800 GPUs in all three stages, with 20 hours for SFT, 15 hours for DPO on LoRA and 8 hours for DPO on PhysEncoder.
The training process employs the Adam optimizer (Kingma, 2014), and we utilize 50 DDIM steps (Song et al., 2020)
and set the CFG scale to 7.5 during inference.

Dataset construction.
For the proxy task, we follow PISA (Li et al., 2025) to use Kubric (Greff et al., 2022) to create synthetic datasets of “free-fall”. The object assets are sourced from the Google Scanned Objects (GSO) dataset (Downs et al., 2022). For generalizability demonstration, we utilize WISA-80K (Wang et al., 2025) encompassing 17 types of real-world physical events across three major branches of physics (Dynamics, Thermodynamics, and Optics).

Evaluation protocols.
PisaBench (Li et al., 2025) is introduced to evaluate our model’s performance on the proxy task.
We use SAM 2 (Ravi et al., 2025) for segmentation of object masks and compute the following metrics between corresponding masks of generated and ground truth videos for evaluation: L2 distance between the centroids of the masked regions, chamfer distance (CD) and Intersection over Union (IoU) of the mask regions.
We utilize VIDEOPHY (Bansal et al., 2024) for evaluating physics awareness of video generation in general open-world scenarios. We test on 344 carefully crafted prompts from it, which reflect a wide array of physical principles, and report the physical commonsense (PC) and semantic adherence (SA) scores.

### 4.2 Evaluation on Proxy Task

**Table 1: Ablation study for models from different training stages and strategies on proxy task, evaluated on the test set split into “seen” and “unseen”. $v_{\theta}$ is DiT model, $E_{p}$ is PhysEncoder.**
| Training Stages | Seen | Unseen | Average |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| L2 ($\downarrow$) | CD ($\downarrow$) | IoU ($\uparrow$) | L2 ($\downarrow$) | CD ($\downarrow$) | IoU ($\uparrow$) | L2 ($\downarrow$) | CD ($\downarrow$) | IoU ($\uparrow$) |  |
| Base | 0.1066 | 0.323 | 0.119 | 0.1065 | 0.339 | 0.111 | 0.1066 | 0.331 | 0.115 |
| SFT for $v_{\theta}$ | 0.0532 | 0.134 | 0.137 | 0.0512 | 0.133 | 0.135 | 0.0522 | 0.134 | 0.136 |
| SFT for $v_{\theta}$ & $E_{p}$ (Stage I) | 0.0568 | 0.141 | 0.137 | 0.0498 | 0.128 | 0.134 | 0.0533 | 0.134 | 0.135 |
| SFT for $v_{\theta}$ + DPO for $v_{\theta}$ | 0.0560 | 0.143 | 0.144 | 0.0446 | 0.115 | 0.128 | 0.0503 | 0.129 | 0.136 |
| SFT for $v_{\theta}$ & $E_{p}$ + DPO for $v_{\theta}$ (Stage II) | 0.0520 | 0.125 | 0.133 | 0.0454 | 0.116 | 0.136 | 0.0487 | 0.120 | 0.134 |
| SFT for $v_{\theta}$ & $E_{p}$ + DPO for $E_{p}$ | 0.0559 | 0.140 | 0.138 | 0.0503 | 0.129 | 0.134 | 0.0531 | 0.134 | 0.136 |
| SFT for $v_{\theta}$ & $E_{p}$ + DPO for $v_{\theta}$ & $E_{p}$ | 0.0501 | 0.124 | 0.141 | 0.0458 | 0.121 | 0.140 | 0.0480 | 0.123 | 0.141 |
| \rowcolorshadecolor SFT for $v_{\theta}$ & $E_{p}$ + DPO for $v_{\theta}$ + DPO for $E_{p}$ (Stage III) | 0.0489 | 0.120 | 0.141 | 0.0450 | 0.115 | 0.145 | 0.0470 | 0.118 | 0.143 |
| SFT for $v_{\theta}$ & $E_{p}$ + DPO for $v_{\theta}$ + DPO for $v_{\theta}$ & $E_{p}$ | 0.0477 | 0.121 | 0.144 | 0.0452 | 0.115 | 0.141 | 0.0466 | 0.118 | 0.142 |

Figure: Figure 2: Qualitative comparison with video generation models specialized for rigid-body motion proves the advantage of our model in shape consistency and trajectory accuracy on “free-fall”.
Refer to caption: x2.png

Figure: Figure 3: Qualitative ablation for models in each training stage on proxy task. The model exhibits a preliminary capability for predicting object motion trends after SFT. Two-stage DPO further improves model performance in preserving objects’ rigidity and complying with physical laws (e.g., gravitational acceleration and collision). $v_{\theta}$ is DiT model and $E_{p}$ is PhysEncoder.
Refer to caption: x3.png

To validate that our training pipeline can effectively improve the physical performance of base model on the proxy task, we compare the physical accuracy of our model on ”free-fall” motion with existing works and ablate different training techniques of PhysEncoder.

Comparison.
We compare our model with PhysGen (Liu et al., 2024b) and PISA (Li et al., 2025) on the real-world subset from PisaBench (Li et al., 2025) which is unseen to any model during training for robust evaluation. We apply more rigorous metrics of similarity over all objects in the scene by comparing against the ground truth.

Figure: Table 2: Quantitative comparison with video generation models specialized for rigid-body motion verifies superiority of our model on proxy task.
Refer to caption: x4.png

Table [2](https://arxiv.org/html/2510.13809v1#S4.T2) shows that our model outperforms both baselines. PhysGen struggles with accurately modeling spatial relationships between objects and surfaces like ground or tables, thus often leads to physically implausible object interactions. For PISA, its best variant with depth-based reward optimizes for trajectory accuracy (comparable L2/CD) at the cost of shape consistency (lower IoU). In contrast, ours excels in IoU while maintaining competitive trajectory accuracy, achieving the best overall performance, which is also proved in Figure [2](https://arxiv.org/html/2510.13809v1#S4.F2).

Ablation study.
We report the qualitative results from different training stages and pipelines on the synthetic subset of PisaBench in Table [1](https://arxiv.org/html/2510.13809v1#S4.T1),
where block 1 denotes the I2V base model; block 2 - 4 refer to our model and its variants in Stage I - III of training pipeline.
“Seen” corresponds to a split of videos with objects and backgrounds seen during training, and “Unseen” with novel objects and backgrounds.
1) Ablation for different training stages.
row 3, 5, and 8 indicate that our SFT endows the model with preliminary ability to predict objects’ motion of “free-fall”, the subsequent DPO for DiT model further steers the generated videos’ distribution towards physically plausible paths, and the optimization of PhysEncoder in the last stage improves its capability in guiding model towards higher level of physics-awareness. The qualitative results in Figure [3](https://arxiv.org/html/2510.13809v1#S4.F3) consistently prove our pipeline’s efficacy.
2) Ablation for PhysEncoder.
The comparative pipeline in row 2 and 4 is not equipped with PhysEncoder, thus SFT and DPO are both implemented on the DiT model. Although the performance after SFT on both DiT and PhysEncoder (row 3) is even worse than SFT on DiT alone (row 2), showing that single SFT cannot help PhysEncoder learn appropriate physical representations for guiding the DiT model towards physics-awareness, DPO unlocks PhysEncoder ’s potential to extract physical information and guide the model to generate videos with better physical performance (row 4, 5,8).
3) Ablation for DPO strategies.
All strategies of DPO succeed in further improving physical accuracy on average than previous stages.
Only optimizing PhysEncoder (row 6) encounters difficulties in performance improvements. The model itself has not been aligned to adherence to physics before providing feedback to PhysEncoder, preventing DPO from functioning effectively.
Although Stage II of our training pipeline underperforms joint DPO of DiT and PhysEncoder (row 7), our Stage III surpasses all other methods. Joint optimization of DiT and PhysEncoder in Stage III (row 9) achieves comparable overall performances with our Stage III but performs worse on ”unseen” split, probably because this variant with trainable DiT is more likely to overfit training data, harming the model’s generalizability to novel scenarios.

PCA analysis.
We also visualize the principal component analysis (PCA) on the physical features from PhysEncoder in Stage I and Stage III in Figure [4](https://arxiv.org/html/2510.13809v1#S4.F4). In
our Stage III physical feature maps, similarities are shown clearly for objects under the same external forces, (green for objects in mid-air and only affected by gravity, red for objects on the ground subjected to the support force); differences are also shown more obviously between materials (e.g., deformable object in white box has clearly distinct colors), which proves two aspects of physical understanding of our PhysEncoder.

### 4.3 Generalization on General Open-world Scenarios

Figure: Figure 5: Qualitative comparison with existing T2V models on general open-world scenarios including objects of various materials and in different environments, validates the generalizability of our method.
Refer to caption: x5.png

Figure: Figure 6: Qualitative ablation for models in different stages on general open-world scenarios. DPO following Stage I improves the physical coherence of model in Stage III (e.g., fluid mechanics and gravitation).
Refer to caption: x6.png

PhysEncoder demonstrates its physics-awareness for enhancing model’s physical realism on the proxy task, suggesting its potential to generalize to broader open-world scenarios.
We apply our training pipeline on a large-scale dataset (Wang et al., 2025) broadly covering common physical phenomena observed in real world to substantiate the generalizability of our method.

Comparison. We compare with two types of video generation models, general models including HunyuanVideo (Kong et al., 2024), CogVideoX-5B (Yang et al., 2024c), Cosmos-Diffusion-7B (Agarwal et al., 2025), Wan2.1-T2V-1.3B and specialized physics-focused models represented by PhyT2V (Xue et al., 2024) and WISA (Wang et al., 2025). Table [4](https://arxiv.org/html/2510.13809v1#S4.T4) shows that, although our base model is surpassed by CogvideoX-5B, the base model of WISA, our final model in Stage III achieves state-of-the-art performance on both SA and PC metrics, demonstrating that our proposed method enhances the realism of generated videos physically and semantically. Our model also has a significant advantage in efficiency. It is approximately 70x faster than PhyT2V—an iterative method requiring feedback from VLM, and 8x faster than WISA. Our model generates a 5-second video in just 26 seconds on a single A800 GPU, establishing it as a highly practical solution without sacrificing physical or semantic adherence. Figure [5](https://arxiv.org/html/2510.13809v1#S4.F5) includes qualitative comparison with existing T2V models, demonstrating our superior ability in challenging cases of both rigid-body and fluid motion.

**Table 3: Quantitative comparison with existing video generation models on general open-world scenarios. Our model shows superior performance in both physics-awareness and efficiency.**
| Methods | Inference Time (s) | SA ($\uparrow$) | PC ($\uparrow$) |
| --- | --- | --- | --- |
| HunyuanVideo | 1080 | 0.46 | 0.28 |
| Wan2.1-T2V-1.3B | 180 | 0.49 | 0.24 |
| CogvideoX-5B | 210 | 0.60 | 0.33 |
| Cosmos | 600 | 0.57 | 0.18 |
| PhyT2V | 1800 | 0.61 | 0.37 |
| WISA | 220 | 0.67 | 0.38 |
| Our base model | 23 | 0.59 | 0.29 |
| \rowcolorshadecolor Our final model | 26 | 0.67 | 0.40 |

**Table 5: User study for models from different stages validates the effect of our training pipeline in two selected physical scenarios. Our final model shows superior ability of PhysEncoder in Stage III in enhancing the model’s physics-awareness over the base model and Stage I.**
| Methods | Rigid-body movement | Fluid motion |
| --- | --- | --- |
| Base | 7.8 | 12.2 |
| Stage I | 25.3 | 16.7 |
| \rowcolorshadecolor Stage III | 66.9 | 71.1 |

Ablation study. We conduct ablation analysis to verify the effectiveness of our core component and strategy of training in Table [4](https://arxiv.org/html/2510.13809v1#S4.T4). 1) Effectiveness of PhysEncoder: Compared to our base model (row 1), our final model (row 5) improves SA and PC scores by 0.08 and 0.11. The comparative pipeline is not equipped with PhysEncoder, with SFT (row 2) and the following DPO (row 3) both implemented on the DiT model only. Such a pipeline without PhysEncoder improves SA and PC scores by 0.05 and 0.06, proving the advantage of our proposed PhysEncoder in successfully extracting crucial physical knowledge from the training data and using it to guide the generator toward greater physical realism, which is unattainable by simply applying SFT or DPO to the DiT model alone.
2) Effectiveness of DPO: Simply applying SFT to PhysEncoder (row 2 vs. row 4) does not yield an immediate benefit, suggesting that SFT alone is insufficient for PhysEncoder to learn a useful guiding physical representation. However, DPO unlocks the potential of PhysEncoder, allowing it to effectively translate its learned physical representations into improved generation quality in both physical commonsense and semantic adherence (row 4 vs. row 5).
Additionally, Figure [6](https://arxiv.org/html/2510.13809v1#S4.F6) visualizes the videos generated by our model in Stage I and III, further validating the effectiveness of DPO. 3) Effectiveness of whole training pipeline: Table [5](https://arxiv.org/html/2510.13809v1#S4.T5) includes human preference rates among models from different training stages in two types of real-world scenarios, showing that annotators prefer videos from Stage III model than Stage I model or I2V base model in adherence to physical laws.

## 5 Conclusions

We propose PhysMaster, which learns physical representation from input image for guiding I2V model to generate physically plausible videos. We optimize physical encoder based on generative feedback from a pretrained video generation model via DPO, which proves to enhance the model’s physical accuracy and demonstrate generalizability across various physical processes by injecting physical knowledge into generation, proving its potential to act as a generic and plug-in solution for physics-aware video generation and broader applications.

Limitations.
We rely on human annotators to construct preference datasets for DPO in real-world scenarios, which is costly and time-consuming. Existing AI evaluators, however, have flawed physics knowledge and inherit biases, limiting the scalability of reinforcement learning. Fortunately, our DPO training paradigm is effective even with a small amount of human-labeled data (500 in our experiment), mitigating this limitation.

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## Peer Reviews

confidence: 4
contribution: 2
initial_rating: 4
presentation: 3
questions: Questions:    
（1）Questions About PhysEncoder’s Learned Physical Knowledge  
You state PhysEncoder extracts physical priors from input images and PCA visualization shows grouping by external forces. But do you have evidence that PhysEncoder learns causal physical principles instead of just correlational visual patterns? For example, through counterfactual testing or feature attribution to prove it captures meaningful physical laws? This clarifies if PhysMaster advances physical understanding as claimed.  
（2）Questions About Preference Data Scalability  
You note human annotation for DPO data is costly but dismiss AI evaluators without empirical validation. Have you tested physics-aware AI evaluators against human labels on a WISA-80K subset? What was the agreement rate? If not, why not use hybrid pipelines like AI pre-screening plus human validation to reduce effort? This addresses if the scalability bottleneck is unavoidable.  
（3）Questions About Complex Physical Scenario Experiments  
Your broader scenarios use WISA-80K but lack testing on dynamic multi-force or non-rigid interactions. Why were these excluded given they’re key for generalizability? For synthetic complex scenarios, have you compared generated motion to ground-truth physical quantities instead of just visual metrics? This checks if generalizability extends to challenging cases.  
（4）Questions About Input Noise Robustness  
All experiments use high-quality inputs, but real-world inputs have noise. Have you tested how input corruption impacts PhysEncoder’s performance? If performance degrades, have you tried lightweight preprocessing like denoising? This assesses real-world utility as a plug-in solution.
soundness: 2
strengths: Strengths:   
（1）PhysMaster delivers originality through targeted innovations that address prior limitations: It framesphysical representation as a plug-in module (PhysEncoder) instead of embedding physics into the generation model, decoupling physical knowledge extraction from visual generation—solving the poor generalization of simulation-based methods and overfitting of end-to-end fine-tuning.  
（2）The paper maintains high methodological and experimental quality: The three-stage pipeline logically addresses sequential challenges, using well-justified components.  
（3）The paper is well-organized and accessible.  
（4）PhysMaster has immediate and long-term value: Its plug-in design and efficiency make it practical for content creation and prototyping.
summary: Summary:  
This paper addresses the limitation of existing video generation models—their inability to adhere to physical laws despite producing visually realistic content—by proposingPhysMaster, a framework that enhances physics-awareness through learned physical representations and reinforcement learning (RL).  

Contributions:  
（1）Novel Physical Representation Framework: PhysMaster introduces PhysEncoder to extract implicit physical knowledge from input images, bridging the gap between visual input and physical guidance—addressing the key challenge of translating visual cues to physics-aware generation.  
（2）RL-Driven Optimization for Physical Representation: Unlike prior RL methods that fine-tune the entire video model, PhysMaster uses DPO to specifically optimize PhysEncoder. This avoids overfitting to specific physical scenarios and enhances generalizability across diverse phenomena.  
（3）Efficient and Generalizable Pipeline: The three-stage pipeline (SFT + two DPO stages) balances effectiveness and efficiency. It works with small human-labeled datasets (500 preference pairs suffice) and generalizes from simple "free-fall" to 17 types of real-world physical events.  
（4）SOTA Performance with Practical Value: PhysMaster outperforms existing models in both physical accuracy and inference speed (generating a 5-second video in 26 seconds), making it a plug-and-play solution for physics-aware video generation and potential applications like robotics training or scientific visualization.
weaknesses: Weaknesses:   
（1）Over-Reliance on Human Annotation for Real-World Preference Data Limits Scalability. A core limitation of the work is its dependence on human annotators to construct preference datasets for DPO in real-world scenarios (e.g., WISA-80K). The paper acknowledges this is "costly and time-consuming", but it fails to address how this bottleneck restricts the framework’s scalability to more diverse physical phenomena (e.g., quantum mechanics, electromagnetism) or larger datasets. Worse, existing AI evaluators (e.g., VLMs) are dismissed as "flawed in physics knowledge" without any attempt to validate or improve them—a missed opportunity to reduce human effort.   
（2）Lack of Analysis on PhysEncoder’s Physical Knowledge Granularity. The paper claims PhysEncoder extracts "physical priors like relative positions and potential interactions", but it provides no evidence ofwhat specific physical knowledgethe encoder actually learns.   
（3）Experiments on Complex Physical Interactions Are Insufficient. The paper’s "broader scenarios" evaluation focuses on 17 physical events, but it neglectsdynamic, multi-object, or non-rigid interactions—the most challenging cases for physics-aware generation.

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: 1. In the general-scene setting, how are the positive and negative samples constructed during the DPO stage?
What does the data distribution look like, and what are typical examples?
The authors should clarify these aspects to make the work more reproducible and easier for the community to follow.
soundness: 3
strengths: 1. The paper is well-written and easy to follow.

2. The experimental section is comprehensive, and the results are convincing.

3. The visualizations in the supplementary material effectively demonstrate the qualitative improvements.
summary: This paper proposes PhysEncoder, a module designed to encode physical information as an additional conditioning signal, thereby injecting physical knowledge into the video generation process. Building upon this, the authors further apply the DPO algorithm to enhance the consistency between I2V-generated videos and real-world physical laws. Experimental results show that the proposed method improves video realism under both free-fall and more general scenarios, aligning generated motion with real physical conditions.
weaknesses: The actual contribution of PhysEncoder is not well demonstrated.

1. Quantitative evidence:
In Table 1, the improvement from SFT for vθ & Ep + DPO for Ep (row 6) over SFT for vθ & Ep (Stage I) is relatively limited.
The authors explain this by stating that DiT needs to align its physical perception before PhysEncoder can take effect, and they further provide Stage 3 (PhysEncoder-only optimization) as supporting evidence.
However, the number of optimization steps for each stage is not provided.
It remains unclear whether continued optimization of vθ alone could achieve similar gains, which would weaken the claimed contribution of PhysEncoder.

2. Architectural design:
It is not well justified why the structure of PhysEncoder itself enhances physical reasoning.
Could the same goal be achieved by using the latent code of the first frame with an additional projection head to model physical information more efficiently?
Further analysis or ablation is needed.

3. Feature analysis:
The role of PhysEncoder’s output tokens in visual generation is unclear.
The PCA analysis of physical features is informative, but it would be interesting to see whether similar correlations hold across the entire video sequence.
Specifically, can the physical features distinguish between free-fall and ground-contact states as the object moves?
This deserves further visualization and analysis.

---

confidence: 5
contribution: 2
initial_rating: 2
presentation: 2
questions: Only one addtional question: why RL here is necessary, comparing direct supervized finetuning on WISA-80K?
soundness: 2
strengths: The paper is clearly written, and according to the results from Table 3, it achieved better performance than existing works w.r.t. physics metrics.
summary: The paper proposed to enhance video generation model (I2V) 's obedience to physics rules by introducing an aditional module (named as PhysMaster) to learn physics information implicitly from I and then inject this condition by concatenaing the physical feature after the image latent from VAE. This module is based on DINO v2 and trained by DPO RL with preference data annotated by human. Training data used is from WISA-80K. For experiment, authors compared the RL-tuned I2V model (without any detail) to other general model as well as physics-enhanced model such as WISA/PhyT2V.
weaknesses: 1. The proposed PhysMaster module (initialized with DINOv2) is designed to be responsible for physics information learning. However, it only took image as input, leaving text prompt untouched. With these settings, it's highly doubtful that the information encoded by PhysMaster module is really about physics. It's simple: how can we tell what physical rules would be involved with image given only?
2. The proposed method is not training-free / plug-and-play for video generation model, this means we have to train the PhysMaster module as well as the I2V model (with lora in the paper). And since the whole model is finetuned with physics-centric data, it should definitely perform better on physics oriented metrics. This will make the contribution claims untenable.

---

confidence: 3
contribution: 3
initial_rating: 4
presentation: 2
questions: 1. Could you elaborate on the performance drop in Stage I when introducing the PhysEncoder (Table 1, row 3)? Why does a simple SFT objective fail to effectively utilize the PhysEncoder, and what is the intuition behind why the DPO objective is uniquely capable of correcting this and "unlocking" its potential?
2. The PhysEncoder is initialized from a DINOv2 model pretrained for depth estimation (Depth Anything). This provides a very strong geometric prior. How much of the performance gain is due to this specific initialization versus the DPO training? An ablation study with a PhysEncoder initialized from scratch or with a more general-purpose pretrained model (e.g., standard ImageNet pretraining) would help isolate the contribution of the proposed representation learning framework.
3. For the generalization experiments, the appendix mentions training on a "combined dataset" of simulated and real-world data. Could you clarify if the model is trained from scratch on this combined set, or is it finetuned from the model trained on the proxy task? How well does the physical representation learned purely on the "free-fall" proxy task transfer to the broader, more complex scenarios without this joint training?
soundness: 3
strengths: 1. The paper's primary novelty lies in framing the learning of physical properties as a representation learning problem solved via reinforcement learning. Instead of directly finetuning the entire video model with RL or relying on explicit physics simulators, the proposed method focuses on optimizing a dedicated, plug-in PhysEncoder. This is a creative and elegant approach to inject physical knowledge into generative models.
2. The proposed three-stage training pipeline (SFT, DPO on DiT, DPO on PhysEncoder) is well-structured and methodologically sound. The experimental evaluation is comprehensive, including a controlled proxy task with clear metrics (CD, IoU) and generalization experiments on a diverse real-world dataset (WISA-80K). The ablation studies are thorough, effectively justifying key design choices like the training strategy and model components.
summary: This paper proposes PhysMaster, a novel framework to improve the physical plausibility of generated videos. The core idea is to introduce a PhysEncoder that extracts a "physical representation" from the initial image to guide an image-to-video (I2V) diffusion model. Since this physical representation is not explicitly defined, it cannot be directly supervised. The authors overcome this by employing a reinforcement learning approach, specifically Direct Preference Optimization (DPO), to train the PhysEncoder. The physical plausibility of the generated videos serves as feedback, optimizing the encoder to produce representations that lead to more physically realistic outcomes. The method is evaluated on a "free-fall" proxy task and then generalized to broader, more diverse physical scenarios, demonstrating improved performance over existing methods.
weaknesses: 1. The learned "physical representation" remains a black box. While the PCA visualization in Figure 4 provides some insight, it is still high-level. It is difficult to ascertain whether the model is learning fundamental physical principles (like mass or friction) or simply learning complex correlations that work well for the training distribution. This makes the claim of "mastering physical representation" strong.
2. The ablation study in Table 1 (row 3 vs. row 2) shows that initial Supervised Fine-Tuning (SFT) with the PhysEncoder actually degrades performance compared to SFT on the base model alone. The paper states that DPO later "unlocks" the potential, but this initial negative result suggests the SFT stage might be suboptimal or that the integration of the encoder is non-trivial.
3. For the broader scenarios, the DPO training relies on a preference dataset of only 500 human-annotated pairs. While the authors note this as a success in data efficiency, it raises questions about the robustness and generalizability of the learned policy across the vast space of real-world physical interactions. The model might overfit to the specific types of physical phenomena present in this small dataset.