Compositional 4D Dynamic Scenes Understanding with Physics Priors for Video Question Answering

## Abstract

Abstract For vision-language models (VLMs), understanding the dynamic properties of objects and their interactions within 3D scenes from video is crucial for effective reasoning. Cognitive science research has suggested that humans are adept at understanding these properties by constructing 3D and temporal (4D) representations of the world. In this work, we introduce a video question answering dataset SuperCLEVR-Physics that focuses on the dynamics properties of objects. We concentrate on physical concepts — velocity , acceleration , and collisions within 4D scenes, where the model needs to fully understand these dynamics properties and answer the questions built on top of them. From the evaluation of a variety of current VLMs, we find that these models struggle with understanding these dynamic properties due to the lack of explicit knowledge about the spatial structure in 3D and world dynamics in time variants. To demonstrate the importance of an explicit 4D dynamics representation of the scenes in understanding world dynamics, we further propose NS-4Dynamics , a N eural- S ymbolic model for reasoning on 4D Dynamics properties under explicit scene representation from videos. Using scene rendering likelihood combining physical prior distribution, the 4D scene parser can estimate the dynamics properties of objects over time to and interpret the observation into 4D scene representation as world states. By further incorporating neural-symbolic reasoning, our approach enables advanced applications in future prediction, factual reasoning, and counterfactual reasoning.
Our experiments show that our NS-4Dynamics suppresses previous VLMs in understanding the dynamics properties and answering questions about factual queries, future prediction, and counterfactual reasoning. Moreover, based on the explicit 4D scene representation, our model is effective in reconstructing the 4D scenes and re-simulate the future or counterfactual events.
Our dataset and model will be released at https://github.com/XingruiWang/SuperCLEVR-Physics

## 1 Introduction

Visual question answering (VQA) is commonly regarded as a comprehensive method to assess how well machine learning models can identify objects, understand their relationships, and perform reasoning over the scenes. When it comes to dynamic scenes in video question answering (VideoQA), we argue that a model should incorporate a dynamic representation that captures how objects change and interact over time. This is partly motivated by studies in cognitive science ; which found that humans excel at understanding the dynamics and interactions in the physical world. This allows humans to perform complex tasks such as planning and manipulation in the 3D world.

However, current video understanding models in the computer vision community focus on VQA tasks in large benchmark datasets of real images. These datasets lack annotations of 3D/4D properties (which are notoriously hard to annotate), which makes it impractical to train these models to estimate 3D/4D annotations. Although these LVMs may have the capability of learning knowledge of 3D/4D properties, perhaps encoded implicitly, it is impossible to test this on these datasets. This applies also to the most recent algorithms which exploit the enormous strengths of large language models.

Cognitive scientists have developed an impressive variety of synthetic datasets for physical reasoning which enable the development and testing of models to understand dynamics, physical properties, and interactions. This has led in many important conceptual findings ; ; ; ; ; ; ; . Due to the nature of these datasets and in particular the simplicity of the objects and environments, it is nontrivial to extend their models to the complexity of real world. As listed in Table 1, their environments are limited either to 2D flat planes or overly simplified objects, leading to a big gap between controlled synthetic datasets and real image datasets that computer vision community favors.

**Table 1: Comparisons with previous dynamical reasoning datasets. From left to right: 3D space, physical dynamics, language understanding, realistic objects.**
|  | 3D | Phy. | Lang. | Real |
| --- | --- | --- | --- | --- |
| CRAFT | ✗ | ✗ | ✓ | ✗ |
| CLEVRER | ✗ | ✗ | ✓ | ✗ |
| Comphy | ✗ | ✓ | ✓ | ✗ |
| Physion | ✓ | ✗ | ✗ | ✗ |
| Physion++ | ✓ | ✓ | ✗ | ✗ |
| Ours | ✓ | ✓ | ✓ | ✓ |

To address the understanding of the 4D dynamic in Video Question Answering (VideoQA) tasks, we introduce SuperCLEVR-Physics.
This dataset focuses on the dynamics and interactions of objects in 3D space — velocities, accelerations, and collisions, and design questions around these properties in the factual, future prediction, and counterfactual scenario. As opposed to the simple objects in previous datasets ; , we improve the realism of the appearances of objects and backgrounds with new materials and textures.. For video generation, we first design the dynamics properties in the initial state and use PyBullet  to simulate the motion and interaction in the following frames. Then we use Blender  to render the videos. Questions are formulated around dynamic properties and interactions from three perspectives: factual, predictive, and counterfactual. Factual questions are answered using the full video frames, predictive questions are answered based only on the initial portion of the video, and counterfactual questions rely on modified properties that differ from the original video. Fig.1 presents examples of video frames and their corresponding factual, predictive, and counterfactual questions. As a compositional reasoning dataset, we also provide new operation programs to answer these questions step by step using a 4D dynamic scene representation.
We test the existing VideoQA models and their variants on SuperCLEVR-Physics and find they are struggling to understand these dynamic features, due to the lack of explicit knowledge of the 3D world or 3D dynamics of objects.

This motivates us to develop NS-4Dynamics, a neural-symbolic model that reasons about dynamics by first estimating explicit 4D scene representation. Our NS-4Dynamics  consists of two key modules, a dynamic 3D compositional model following by a symbolic reasoning module. The dynamic 3D compositional model learns a 3D generative model ; of the scene and captures the dynamics of objects with a physical prior to output a 4D parse of the video..
Then based on the compositional 4D scene representation, the symbolic reasoning module executes the reasoning program and obtains answers step-by-step with high interpretability.

We demonstrate that NS-4Dynamics outperforms previous state-of-the-art models on video question answering regarding the dynamic properties of objects, including velocity, acceleration, and collision events on our SuperCLEVR-Physics. Furthermore, with an explicit 4D scene representation, our model not only infers objects’ dynamic properties and reconstruct the scenes, but also predicts future states and reasons about counterfactual conditions.

Our contributions :(1) First, we introduce a VideoQA dataset regarding the dynamics properties of objects (velocity, acceleration) and multi-object interactions (collisions). We also improve the visual complexity of objects and backgrounds with new color or textures for materials, and further design questions about these properties in factual, future prediction, and counterfactual scenarios. (2) Second, we propose NS-4Dynamics, a neural-symbolic model that first reconstructs the 4D scene with a dynamic 3D generative model with physical priors as the perception module, and then reasons about dynamics over the explicit 4D scene representation. Third, we demonstrate that NS-4Dynamics can outperform previous state-of-the-art models VLMs on SuperCLEVR-Physics, showing the advantages of our approach to infer objects’ dynamic properties, reconstruct the scenes, imagine future states, and reason about counterfactual conditions.

Figure: Figure 1: Examples of video frames and their corresponding factual, predictive, and counterfactual questions.
Refer to caption: x1.png

## 2 Related Work

#### Video question answering.

Video questions answering (VideoQA) is a challenging task because models must not only detect and identify objects from static images, but also track and infer objects’ changes and interactions over a sequence of frames. A number of VideoQA datasets annotate question-answer pairs on natural videos ; ; . However these datasets are not suitable for studying dynamic physical properties as natural videos contain limited object interactions and the annotated questions are hindered by shortcuts and biases. Another line of works focused on physical reasoning in simulated environments ; ; ; . However, these datasets are either built in restricted settings ; or lack of real dynamics and forces common in real world (see Table 1). In order to further challenge and explore the limitations of current video-text models on dynamics reasoning, we develop SuperCLEVR-Physics  with improved realism for both video quality and physics simulation.

#### Video-text models.

With the availability web-scale image-text or video-text paired datasets ; , recent video-text models ; ; adopted heavy multi-modal pretraining and achieved improved results on a wide range video understanding tasks.
However, these models often exploit biases and shortcuts for dynamic reasoning, failing to capture the physical intrinsics that drive object movements and interactions. Our NS-4Dynamics  incorporated a 4D dynamic scene representation in our scene parser, allowing compositional reasoning of various physical events in the video sequence.

#### Physical scene understanding.

Understanding the physical events in a dynamic 4D scene or inferring the future state is hard as it requires a model to apply physical principles to explain the observed events or simulate the future outcomes. Previous studies explored physics engines for simulation and learning ; , or integrating a differentiable physics engine in deep learning models ; ; . Another line of works learned physical properties from a compositional scene ; ; ; ; . However, previous methods were limited to simple scenes where physics models provide good approximations of the world. We extend our scope to more realistic scenes with real-world objects and complex simulations, and enable physical scene reasoning with our dynamic 3D compositional reasoning.

#### 3D compositional models.

Our work is also related to previous 3D compositional models for image classification , 6D pose estimation , and 3D-aware visual questions answering . 3D compositional models learns a probabilistic generative model of feature activations of a scene and enables scene understanding with analysis-by-synthesis. We extended previous 3D compositional models with a dynamic scene representation, allowing us to reconstruct object trajectories and reason about the physical events.

## 3 Dataset

To study the understanding of dynamics in visual question answering, we present the SuperCLEVR-Physicsdataset. This dataset is designed to evaluate the understanding of physical object dynamics — including velocity, acceleration, and collision events in Video Question Answering (VideoQA). Our work builds on , which introduced complex scenes with multiple object categories to image-based VQA and which expanded the former with 3D aware scene understanding questions.
We follow the established setting in terms of objects classes and shapes, and extend this line of work to video data, where we include the dynamic properties of objects to build a 4D scene representation.

In this section, we first introduce the three key components for designing the video data in our proposed dataset: (1) A more realistic appearance for the objects and the background in Sec.3.1; (2) The introduction of object dynamics over time in Sec.3.2; and (3) the introduction of dynamic events caused by object interactions in Sec.3.3.
Building on this setting, we will then discuss the physics simulation and video rendering in Sec.3.4 and the automated generation of questions in Sec.3.5.

Figure: Figure 2: (a) We keep the same shape from SuperCLEVR but repaint the color of the material for objects to make it more realistic. (b) We project the environment map from HDRI into a dome shape. (c) After assigning dynamics properties, we use physical simulation (PyBullet) and render (Blender) to generate the video data, where objects are moving and interacting in the dynamic 3D space.
Refer to caption: x2.png

### 3.1 Realistic Appearance

#### Objects appearances

We use the same object shapes as in SuperCLEVR, including 5 vehicle categories (car, plane, bicycle, motorbike, bus) with 18 sub-types. However, like other synthetic datasets in VQA , the original SuperCLEVR colors are simple monochrome.
To achieve higher realism while retaining distinct color labels, we replace the 3D model material with a more realistic colormap by following steps.
(1) We generate a new colormap for all color labels (gray, red, brown, yellow, green, cyan, blue, and purple), with each color including six randomly generated HEX codes created by an LLM.
(2) We then apply these new colors to the main body of the objects. For example, as shown in Figure 2 (a), we choose the largest material area, like a car’s body, and apply a randomly selected HEX code from the colormap. For other parts, we use colors that enhance realism: transparent for windows, black for wheels, and red for lights.

#### Backgrounds

We use highly realistic background images and lights in our dataset. We utilize real HDRI images ([https://polyhaven.com/hdris](https://polyhaven.com/hdris)), containing real captured images with environment maps in 509 different scenes. We map the image as material into a dome shape as background to introduce more variability into data (see Fig. 2 (b)).

### 3.2 Dynamics Properties

A critical feature of our dataset is the dynamic scene properties designed to test the 4D physical scene understanding of vision-language models.
We define these properties to accurately reflect real-world physics, thereby creating a realistic and controlled setting for VideoQA challenges. This section details how we establish and manipulate each dynamic feature to create diverse and complex scenarios for models to interpret. Detailed settings are provided in the Appendix.

#### Position and Orientation.

(1) Position: Objects are placed randomly in the scene.
we use a beta distribution along the x and y axes to make then closer to the edge. The z-axis, which controls height relative to the ground, is set differently to classes. For airplanes, they have the option to start in the air, where the z-axis positions range from 2 to 4.5; otherwise, they are positioned on the ground, same as the other classes.
(2) Orientation: All objects are initially facing the center of the scene and we add a random noise for variability.

#### Velocity.

Each object begins with a predefined velocity state — static (0 m/s), slow (3 m/s), or fast (6 m/s) — setting the initial conditions for movement and subsequent interactions.

#### Internal Forces and Acceleration.

In our dataset, the velocities of objects are further influenced by both internal and external forces, which directly impact their acceleration and subsequent motion, akin to real-world scenarios as follows. (1) Engine Force for Vehicles: Vehicles are equipped with engines that provide forward acceleration. We apply a uniform acceleration of 1 m/s^2 to vehicles with an active engine. We also set speed limits for objects with different velocity states — 8 m/s for "fast" objects and 5.5 m/s for others. (2) Floating Force for Airplanes: Airplanes may have a floating force to either maintain a static height or descend due to gravity without the floating.

#### External Forces and other physical properties.

The environment also includes external forces such as gravity, friction, and elastic forces during collisions, along with additional physical properties of the objects. For further details, please refer to the Appendix.

### 3.3 Collision Events

In our dataset, we extend prior benchmarks ; by focusing on more complex shapes and motions in full 3D space, which makes the motions of objects harder to track after the collision.
The collision event will be calculated by a physics simulator. For each collision, we will record the objects involved and the frame index, allowing us to develop questions from these annotations.

### 3.4 Physical Simulation and Rendering

The video data is generated using the pipeline from the Kubric  package, which uses the physical engine (PyBullet ) for simulation and Blender  to render videos frame by frame. In our dataset, each video is 2000 ms long, resulting in 120 frames with a corresponding frame rate of 60.
In the final dataset, we generate 1000 videos for training, 100 for validation and 100 for testing.

### 3.5 Question Generation

To study 3D dynamic reasoning in visual question-answering tasks, we design the questions for the SuperCLEVR-physics dataset to address three aspects: factual, predictive, and counterfactual reasoning, each focused on dynamics like velocity, acceleration, and collision events. The questions are generated by predefined templates with functional programs.
We have 12752 questions totally, where 7850 are factual, 2750 are predictive, 989 are counterfactual.
We provide all the question templates and the corresponding programs in the Appendix.

#### Factual Questions

These questions revolve around the velocity, acceleration, and collisions of objects. Examples include "Is the object moving?" and "Is object A moving faster than object B?" We also incorporate questions by defining a specific moment from the events of collisions happens or objects coming into frames and ask much like how physics defines instantaneous velocity such as "when object A collides with object B", or "when object A comes into the frame". Vertical and horizontal accelerations are also explored, with questions like "Is object A floating in the air?" or "Is the wagon accelerating?"

#### Predictive Questions

Predictive questions are designed to infer future interactions, especially collisions, based on current dynamics and positioning. An example question is “Will object A collide with object B next?”. For predictive questions, only first 30 frames of videos are given to the model.

#### Counterfactual Questions

Counterfactual questions explore changes in object dynamics and how these affect interactions, such as changes in velocity or acceleration. For instance, "If Object A were moving fast, would it collide with Object B?" or "If Object A’s engine stops, would it collide with Object B?" Such questions allow us to assess how models respond to hypothetical modifications, providing deeper insight into their understanding of physical reasoning.

## 4 Model

Figure: Figure 3: Our NS-4Dynamicshas three main components. I: A Neural symbolic scene parser that parses a 4D dynamic scene representation from the video. II: A physical prior providing a prior distribution for current states. III: A program executor that parses questions into reasoning programs and then executes the program over the predicted scene representation to answer the questions.
Refer to caption: x3.png

In this section, we introduce NS-4Dynamics, to tackle the VideoQA task about object dynamics in 3D space. As shown in Fig. 3, our model consists of three main components: a neural symbolic scene parser, a physical prior module and a program executor with question parser.
The neural symbolic scene parser and the physics prior are combined to first parsing the video sequences into a 4D scene representation.
Then, we integrate the language model and reasoning program to answer the questions about the dynamic properties of objects, including velocity, acceleration, and deduce collisions.
In this part, we will introduce the definition of the 4D symbolic scene representation in Section 4.1, then the scene parser with physical prior in Section 4.2, and the language model and reasoning program in Section 4.3 respectively.

### 4.1 4D Symbolic Scene Representation

For a given video, we denote the image frame at timestep $t$ as $\mathcal{I}_{t}$. The 4D symbolic scene representation $\mathcal{S}$ is a sequence of objects states $\mathcal{S}_{t}=\{O_{t}^{1},O_{t}^{2},\ldots,O_{t}^{N}\}$ over time, where $N$ is the number of objects in the scene. Object $O_{t}^{i}$ is defined by its translation $T_{t}^{i}\in\mathbb{R}^{3}$, rotation $R_{t}^{i}\in\mathbb{R}^{3}$, velocity $v_{t}^{i}\in\mathbb{R}^{3}$, acceleration $a_{t}^{i}\in\mathbb{R}^{3}$, class $cls^{i}$ and color $c^{i}$.

### 4.2 Dynamic Scene Parser with Physics Prior

The scene parser aims to estimate the world state $\mathcal{S}_{t}$
from the corresponding observation $\mathcal{I}_{t}$ and the previous states $\mathcal{S}_{<t}$ with a probabilistic model.
Similar to the pose estimation on images ; , we first use render-and-compare on the feature level for each frame, (*i.e*. $F(\mathcal{I}_{t})$) and maximizing a scene likelihood to estimate the states, where $F(\cdot)$ is a CNN backbone.
We further consider the temporal consistency and physical plausibility between frames by adding a physical prior in the likelihood function for correction. This maximizing likelihood for world states estimation can be written as:

$$ $\displaystyle\hat{\mathcal{S}_{t}}=\arg\max_{\mathcal{S}_{t}}p\left(\mathcal{S }_{t},F(\mathcal{I}_{t})\right)\cdot p(\mathcal{S}_{t}|\mathcal{S}_{<t})$ (1) $$

Preliminaries.
Like pose estimation, the scene likelihood compares the features of images ($\mathcal{I}_{t}$) and the neural mesh model ($M_{cls}$) from soft rasterization , to estimate the 3D location ($T_{t}$) and rotation ($R_{t}$) by maximizing likelihood (*i.e*. world states $\mathcal{S}_{t}$). The neural mesh model represents 3D CAD model as a vertex set and is trained with constructive learning with images to obtain a representation of each vertex. The details of the definition of the neural mesh model and calculation of scene likelihood are put in the Appendix.
During inference, with a given translation $T_{t}$ and rotation $R_{t}$, the neural mesh model of category $cls$ can be projected to have its features $f_{cls}(R,T)$.

#### Inference as scene parsing

As the input images are processed by the CNN and get a feature $F_{t}\coloneq F(\mathcal{I}_{t})$, following the similar structure ; , we are able to compare the features and construct a likelihood function as:

$$ $\displaystyle p(\mathcal{S}_{t},F(\mathcal{I}_{t}))\simeq p(F_{t}\mid M_{cls}, R_{t},T_{t},B),$ (2) $$

where $B$ is a background parameter learned from training.

#### Physical prior

Scene likelihood alone is insufficient to reconstruct a physically plausible 4D scene. Inspired by the idea from bayesian inference area, we integrate a physics prior $p(\mathcal{S}_{t}|\mathcal{S}_{<t})$ into the likelihood function as a correction term as in Eq. 4

However, it’s nontrivial to directly model the physical functions. Many previous studies on physical engines within neural networks only focus on simple shapes like cubes as the complexity in shapes of objects and the interaction process is hard to model. On the other hand, computational physical engine excel at modeling physical functions for any known shapes but are not differentiable and challenging to integrate into the de-rendering process during inference.

In our method, we modify the discriminative physics engineering into a probabilistic model by introducing an uncertainty term $\epsilon\sim\mathcal{N}(0,\sigma^{2})$. Denoted a physics engine as $\text{PE}(\cdot)$, then the prior $p(\mathcal{S}_{t}|\mathcal{S}_{<t})$ can be computed as:

$$ $\displaystyle p(\mathcal{S}_{t}|\mathcal{S}_{<t})$ $\displaystyle=q(R_{t},T_{t}\mid\hat{R}_{t-1},\hat{T}_{t-1}),$ (3) $\displaystyle(R_{t},T_{t})\mid(\hat{R}_{t-1},\hat{T}_{t-1})$ $\displaystyle\sim\mathcal{N}(\text{PE}(\hat{R}_{t-1},\hat{T}_{t-1}),\sigma^{2} I),$ (4) $\displaystyle q(R_{t},T_{t}\mid\hat{R}_{t-1},\hat{T}_{t-1})$ $\displaystyle=C\exp\left(-\frac{1}{2\sigma^{2}}\left[(R_{t},T_{t})-\mu\right]^ {T}\left[(R_{t},T_{t})-\mu\right]\right),$ (5) $$

where $\mu=\text{PE}(\hat{R}_{t-1},\hat{T}_{t-1})$, $C=1/\sqrt{(2\pi)^{k}|\sigma^{2}I|}$.

Finally, the rotation $R_{t}$ and translation $T_{t}$ can be estimated by maximizing the joint likelihood of the rendering likelihood and the physical prior likelihood:

$$ $\displaystyle\hat{R}_{t},\hat{T}_{t}$ $\displaystyle=\arg\max_{R_{t},T_{t}}p(F_{t}\mid O_{c},R_{t},T_{t},B)\cdot q(R_ {t},T_{t}\mid\hat{R}_{t-1},\hat{T}_{t-1}).$ (6) $$

#### Other attributes

By having the estimated world states ($R_{t},T_{t}$), here we introduce how the other properties and interactions are predicted:
(1) Velocity and acceleration can be calculated from the difference between the translation and further between the computed velocities at two consecutive time steps. To avoid the potential noise in the translation estimation, we use a moving average filter to smooth the velocity and acceleration estimation with the window size 5.
(2) Object class can be obtained from the category of mesh model achieving the highest likelihood. To predict the color attribute of objects, we crop the region containing the object from the RGB image and train an additional CNN classifier.

#### Collisions and resimulation

Collisions are obtained from the physics engine simulator as the time of estimating the physical prior. For each collision, we record the time and objects involved.
Predictive and counterfactual resimulation: From the explicit 4D scene representation, we can easily simulate the future or counterfactual states of the objects. For either two cases, we apply the physics engine and assign the predicted translation, rotation, velocity or counterfactual condition to all objects as initial conditions and simulate. We visualize the resimulation results in Sec. 5.4

### 4.3 Program execution

After having the 4D scene representation from the scene parser, we can parse the question into reasoning programs and execute the program on the scene representation to predict the answer. The question parsing follows previous work (; ), where an LSTM sequence-to-sequence model is trained to parse the question into its corresponding program.

## 5 Experiments

### 5.1 Experiment Setup

#### Baseline models.

We select 7 representation models from the following 3 categories as baseline models on SuperCLEVR-Physics. (1) Simple classification-based methods: CNN+LSTM and FiLM. (2) Neural symbolic models: NS-DR and PO3D-VQA, (3) large pretrained video-text models: Video-LLaVA, PLLaVA, and InternVideo. Please refer to the appendix for detailed implementations.

#### Dynamic scene parser implementation.

The dynamic scene parser and CNN classifier are trained on the images and 120k images are used for training. The dynamic scene parser uses ResNeXT for the feature extractor and is trained for 50 epochs with batch size 4 on 4 GPUs. The attributes classifier uses ResNet50 and is trained with the cropped images by the ground truth bounding box. During inference, we set the variance of the physical prior as 3.

### 5.2 Video Question Answering Results

We first compare the NS-4Dynamics with baseline models for the VideoQA task of SuperCLEVR-Physics, the results are shown in Tab. 2.

**Table 2: Performance on the SuperCLEVR-Physics testing split for each question type, *i.e*. factual, predictive and counterfactual. The factual questions contain sub-type about Velocity, Acceleration, Collision and "All" means its overall accuracy. ^† indicates GPT-assisted zero-shot evaluation.**
|  | Average | Factual | Predictive | Counterfactual |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- |
|  | All | Vel. | Acc. | Col. |  |  |  |
| CNN+LSTM | 48.03 | 40.63 | 41.71 | 56.79 | 25.37 | 56.04 | 47.42 |
| FiLM | 50.18 | 44.07 | 48.58 | 53.09 | 26.87 | 54.94 | 51.54 |
| NS-DR | 51.44 | 51.44 | 55.63 | 46.34 | 46.86 | - | - |
| PO3D-VQA | 62.93 | 61.22 | 62.21 | 73.17 | 51.20 | 65.33 | 62.24 |
| Video-LLaVA ^† | 36.04 | 33.12 | 31.90 | 56.44 | 17.31 | 42.34 | 32.65 |
| PLLaVA ^† | 62.18 | 51.45 | 51.90 | 60.12 | 43.75 | 65.69 | 69.39 |
| InternVideo | 52.62 | 51.07 | 59.29 | 49.08 | 36.06 | 54.74 | 59.18 |
| w/o physics | 75.97 | 79.68 | 81.40 | 81.30 | 74.88 | 78.83 | 69.39 |
| NS-4Dynamics | 82.64 | 87.70 | 88.66 | 83.73 | 88.46 | 85.71 | 74.51 |

#### Comparison with classification-based methods.

The accuracy of CNN+LSTM only reaches 40.63%, 56.04% and 47.42% for factual, predictive, and counterfactual questions; and FiLM achieves 44.07%, 54.94% and 51.54%. The performance of these models is notably lower than that of NS-4Dynamics, which achieves 87.70%, 85.71% and 74.51% for factual, respectively. As classification-based methods generally rely on extracting features through CNNs, we demonstrates significant advancements in handling dynamic content with explicit scene representation.

#### Comparison with the Neural-Symbolic methods

The comparison between the Neural-Symbolic models shows significant performance improvement as the dimension of scene representation increases. PO3D-VQA constructs explicit 3D scene representation, leading to a better estimation to the objects’ position and poses, which is beneficial to infer the dynamics and interaction compared with the 2D presentation in VR-DR (9.78%) for the factual questions. As pushing the dimension into 4D and considering the physical prior knowledge, our NS-4Dynamics achieves 82.64% overall accuracy which is 19.71% higher than PO3D-VQA.

#### Comparison with the Video-LLM

Although Video-LLMs are found to have strong video understanding abilities and generalization abilities, they do not perform as well on SuperCLEVR-Physics. In zero-shot settings, Video-LLaVA obtains an overall accuracy of 36.04%, while PLLaVA achieves a better but limited performance of 62.18%. Even with finetuning, the accuracy of InternVideo is only 52.62%, falling far behind our NS-4Dynamics. Results show existing video foundation models fail to capture physical principles or perform reasoning on complex dynamic scenes.

### 5.3 4D Scene Reconstruction Results

To evaluate the accuracy of reconstructed scene representation with the observation, we using the metric is 6D pose estimation to compare the accuracy of the translation and rotation estimation, and compare with the image level 6D pose estimation in PO3D-VQA, and the dynamics scene representation without the physics prior (w/o physics).
The results are shown in Tab. 3.

**Table 3: Evaluation of scene representation by the metric in 6D pose estimation.**
|  | FLOPs | Translations | Rotations |  |  |
| --- | --- | --- | --- | --- | --- |
|  | RMSE | $Acc_{\frac{\pi}{18}}$ | $Acc_{\frac{\pi}{6}}$ | $\text{Med}_{err}$ |  |
| PO3D-VQA | 0.18 | 0.233 | 62.21 | 67.21 | 3.498 |
| w/o physics | 0.63 | 0.055 | 78.73 | 96.18 | 2.730 |
| NS-4Dynamics | 0.62 | 0.040 | 89.71 | 98.34 | 1.419 |

### 5.4 Qualitative results

We visualize how our model and baseline w/o physics reconstruct the scenes and re-simulate the future scene in predictive questions. In Fig. 4, the questions ask about the collision, but in (c) the baseline misses the position of the car and makes mistakes. In Fig. 5, from the input videos (a1), our model predicts a better position for the blur bus (b1); By resimulation, the result of our model (b2) is closer to the ground truth future frames (a2), compared with the results of baseline (c2).

Figure: Figure 4: Qualitative examples of factual questions. (a) shows the input video; (b)We show our NS-4Dynamics can have a better estimation for the motion with the physical prior. (c) The error of position predicted by baseline w/o physics in the red box leads to the mistake in answering the question.
Refer to caption: x4.png

Figure: Figure 5: Qualitative examples of predictive questions. (a1) The first 30 frames are given to models as input video and (a2) the following are hidden as ground truth future states; (b1) our NS-4Dynamics has a better estimation of the poses of objects, and (b2) a plausible imagination by re-simulation. (c1) The red box shows the error of poses estimation of bus from the baselinew/o physics, which (c2) makes the red suv collide with the school bus in the future.
Refer to caption: x5.png

## 6 Conclusion and Discussion

In this work, we study the 3D dynamics properties in video question answering. We present a new dataset SuperCLEVR-Physics which builds a realistic 3D simulation with various physical events. We find that existing video-text models, even with large-scale pretraining, fail to capture important physical properties that are crucial for temporal or predictive reasoning. Hence we develop NS-4Dynamics, a neural-symbolic model built on explicit 4D scene representation. We capture the physical principles by incorporating physical priors in our neural symbolic scene parser. Experimental results on SuperCLEVR-Physics show that our approach outperforms previous state-of-the-art methods by a wide margin, demonstrating the advantages of our approach to infer dynamic properties and to predict future states. In ongoing work we are making our dataset more realistic by exploiting big data techniques, such as generating 3D-DST data with diffusion models and developing our neural-symbolic parser to work on real images by extending .

#### Limitations.

Our neural symbolic scene parser integrates physics as a prior when predicting future states. For future work we hope to explore differentiable physics engines that enable scene parsing with multi-frame reasoning. This is a significant extension of previous methods as we are involving real-world objects and complex simulations, posing challenges to the differentiable physics simulator.

## Appendix A Dynamic properties setting

**Table 4: Dynamic settings of SuperCLEVR-Physics**
| Attribute | Description | Aeroplanes | Others |
| --- | --- | --- | --- |
| Mass | Calculated from volume | $\rho V$ |  |
| Position | $(x,y)$ | Beta distribution |  |
| $z$ | Uniform distribution | 0 |  |
| Orientation | Faces to the center with noises | - |  |
| Velocity | Initial state: static, slow, fast | $\{0,3,6\}$ m/s |  |
| Internal Force | Engine force (Forwards) | $\{1,0\}\text{m/s}^{2}$ |  |
| Floating force(Upwards) | {10, 0} $\text{Mass}\times\text{m/s}^{2}$ | 0 $\text{Mass}\times\text{m/s}^{2}$ |  |

#### External Forces and other physical properties

- •
Gravity: All objects in the dataset are subject to gravity, which influences their vertical movements and impacts when in flight or during falls. The gravitational constant is set as 10.
- •
Friction: Frictional forces affect the movement of all objects, especially when they interact with the ground or each other, slowing them down and eventually bringing them to a stop.
- •
Elastic Force during Collisions: When objects collide, the elastic forces come into play, defined by elasticity coefficients. These forces affect how objects bounce off each other or rebound from barriers, significantly altering their trajectories and speeds.

#### Physical Properties

(1) Mass: The mass for each object is calculated based on its specific shape model, which is linearly related to its volume with a density of 2.7;
(2) Friction: The friction of objects are set as xx; the friction floor (the dome shaped background) is set as xx.
(2) Restitution: The restitution of objects are xx and the floor’s are set to xx.

## Appendix B Question and program executor

We design new program for the 4D dynamcis reasoning VideoQA. For the factual question, we use query_moving,query_moving_fast,query_moving_direction for get the moving status. greater_linear_velocities, less_linear_velocities for comparing the linear velocities. query_engine for querying the physics engine. query_floating for querying the floating objects. filter_collision for filtering the collision events of given objects.get_col_partner for getting the collision partner of the object. get_all_col_partners for getting all the collision partners of the object.

## Appendix C Details of Dynamic Scene Parser with Physics Prior

The ultimate goal of the scene parser is to estimate the all dynamic state $\mathcal{S}_{t}$
from the corresponding observation $\mathcal{I}_{t}$ and the previous states $\mathcal{S}_{<t}$ using a probabilistic model. The previous work in 6D pose estimation has proposed to use render-and-compare to maximizing scene likelihood for static images. For the video data, we further consider the temporal consistency and physical plausibility of the dynamics estimation.

$$ $\displaystyle\hat{\mathcal{S}_{t}}$ $\displaystyle=\arg\max_{\mathcal{S}_{t}}p\left(f(\mathcal{I}_{t})|\mathcal{S}_ {t}\right)\cdot p(\mathcal{S}_{t}|\mathcal{S}_{<t}),$ (7) $\displaystyle\hat{\mathcal{S}_{0}}$ $\displaystyle=\arg\max_{\mathcal{S}_{0}}p(f(\mathcal{I}_{0})|\mathcal{S}_{0}).$ (8) $$

Inspired by the neural-meshed based generative mode for pose estimation methods on static images [, ], we develop a de-rendering based generative model to reconstruct the 4D dynamic scene representation frame by frame. To ensure the predictions are not only plausible in 3D positions but also in physical rules, we incorporate a physical likelihood model and a 3D generative model with rendering likelihood, as shown in Fig. 3.

Preliminaries.
In the previous work for static images[], Neural Meshes model were introduced for 6D pose estimation through inverse rendering. For that task, the goal is to jointly estimate the 6D pose (2D location $(x,y)$, distance $d$ to the camera and 3D pose ($\alpha,\beta,\gamma$) of objects in an image by comparing the Neural Meshes feature after rendering with the input image feature and maximizing the rendering likelihood. More formally, the mesh for a given object in category $c$ is represented as $M_{c}=\{v_{i}\in\mathbb{R}^{3}|i=1\ldots N\}$ where $v_{i}$ means the vertex. The corresponding neural texture of the mesh $M_{c}$ is $T_{c}\in\mathbb{R}^{N\times l}$ where $l$ is the dimension of the feature. So that the neural mesh model for category $c$ is their aggregation $O_{c}=\{M_{c},T_{c}\}$ .

In this work, we use transform the position into a world coordinate instead. Given the object 3D rotation $R=(\alpha,\beta,\gamma)$ and translation $T=(x,y,z)$, we can render the neural mesh model $O_{c}$ into a feature map $F_{c}$ with soft rasterization []:

$$ $\displaystyle F_{c}(R,T)=\mathfrak{R}(O_{c},R,T).$ (9) $$

#### Inference as scene parsing

For static images, the previous work [] has shown that the rendering likelihood can be used to estimate the 6D pose of objects. In our dynamics scene, similar strategies can be applied to each frame at time step $t$.
As the neural mesh models are probabilistic generative model of neural feature activation, we can first define the rendering likelihood of the feature map $F_{t}$ given any 6D pose $R_{t},T_{t}$ as:

$$ $\displaystyle p(\mathcal{I}_{t}|\mathcal{S}_{t})$ $\displaystyle=p(F_{t}\mid O_{c},R_{t},T_{t},B)$ (10) $\displaystyle=\prod_{i\in\mathcal{FG}}p(f_{t}^{(i)}\mid O_{c},R_{t},T_{t}) \prod_{j\in\mathcal{BG}}p(f_{t}^{(j)}\mid B),$ (11) $$

where $\mathcal{FG}$ and $\mathcal{BG}$ are the set of foreground and background locations on the 2D feature map and $f_{t}^{(i)}$ is the feature vector of $F$ at location $i$ at timestep $t$. B is a background parameter learned from training. Here the foreground and background likelihoods are modeled as Gaussian distributions.

#### Physical prior

(correction)

Rendering likelihood alone is insufficient to reconstruct a physically plausible 4D dynamic scene representation. We also integrate a physics prior into our likelihood model. The physical process can be modeled as a Markov model [], where the physical prior distribution of rotation $R_{t}$ and translation $T_{t}$ at time step $t$ can be expressed as:

$$ $\displaystyle p(\mathcal{S}_{t}|\mathcal{S}_{<t})=q(R_{t},T_{t}\mid\hat{R}_{t- 1},\hat{T}_{t-1}).$ (12) $$

However, it’s nontrivial to directly model the physical functions. Most current studies on physical engines within neural networks only focus on simple shapes like cubes as the complexity in shapes of objects and the interaction process is hard to model. On the other hand, computational physical engines such as Bullet excel at modeling physical functions for any known shapes but are not differentiable and challenging to integrate into the de-rendering process during inference.

In our method, we modify the discriminative physics engineering into a probabilistic model by introducing an uncertainty term $\epsilon\sim\mathcal{N}(0,\sigma^{2})$. Denoted a physics engine as $\text{PE}(\cdot)$, then the $q(R_{t},T_{t}\mid\hat{R}_{t-1},\hat{T}_{t-1})$ can be assumed by:

$$ $\displaystyle(R_{t},T_{t})$ $\displaystyle=\text{PE}(\hat{R}_{t-1},\hat{T}_{t-1})+\epsilon,$ (13) $\displaystyle(R_{t},T_{t})\mid(\hat{R}_{t-1},\hat{T}_{t-1})$ $\displaystyle\sim\mathcal{N}(\text{PE}(\hat{R}_{t-1},\hat{T}_{t-1}),\sigma^{2} I),$ (14) $\displaystyle q(R_{t},T_{t}\mid\hat{R}_{t-1},\hat{T}_{t-1})$ $\displaystyle=C\exp\left(-\frac{1}{2\sigma^{2}}\left[(R_{t},T_{t})-\mu\right]^ {T}\left[(R_{t},T_{t})-\mu\right]\right),$ (15) $$

where $\mu=\text{PE}(\hat{R}_{t-1},\hat{T}_{t-1})$, $C=1/\sqrt{(2\pi)^{k}|\sigma^{2}I|}$.

Finally, the rotation $R_{t}$ and translation $T_{t}$ can be estimated by maximizing the joint likelihood of the rendering likelihood and the physical prior likelihood:

$$ $\displaystyle\hat{R}_{t},\hat{T}_{t}$ $\displaystyle=\arg\max_{R_{t},T_{t}}p(F_{t}\mid O_{c},R_{t},T_{t},B)\cdot q(R_ {t},T_{t}\mid\hat{R}_{t-1},\hat{T}_{t-1}).$ (16) $$

#### Relationship to Bayes-Kalman filtering

The conceptual ideas are directly inspired by the classic ideas of Bayes-Kalman filtering where the goal is to estimate a hidden state based on a sequence of observations. Bayes-Kalman filtering consists of updating a probability distribution of the hidden state by a prediction step followed by a correction step which incorporates evidence from a new observation. It uses a dynamic model for how the hidden state changes with time which is directly analogous to our physical prior. It has an observation model, corresponding to our scene parser, for how new observations give evidence for the hidden state. Bayes-Kalman, however, is difficult to implement in complex applications, like ours, because it requires us to represent and update a complex probability distribution. The standard approach for doing this is particle filtering where the probability distribution is represented by a set of point particles which are updated during the prediction and correction steps. This is also challenging so instead we use a simple approximation which essentially uses a single particle. In future work we will experiments to see if our model gives even better results if instead of this approximation we use particle filtering.

## Appendix D Baseline Models

We select 7 representative models from the following 3 categories as baseline models on SuperCLEVR-Physics. (1) Simple classification-based methods encode videos with a CNN backbone and predict answers with a classifier head. Specifically we consider CNN+LSTM that aggregates frame-level CNN features and encode the question by LSTM, and FiLM [] that incorportes a feature-level linear modulation module for question answering. (2) Neural symbolic models first parse the scene into object instances and then executes a program for question answering. NS-DR [] adopts a Mask R-CNN for object detection. We modify it with the new program in SuperCLEVR-Physics and evaluate it on factual questions. As their 2D simulator is unable to reason the objects in 3D, we do not compare it on predictive and counterfactual questions. PO3D-VQA [] uses a 3D detector and reconstructs an explicit 3D scene representation for each frame. We extend the model for VideoQA by computing dynamic properties from object locations and predict collision by filtering the distance between objects. (3) Large pretrained video-text models adopt large-scale image-text or video-text data for pretraining and achieve strong generalization abilites to various video-text tasks. We consider Video-LLaVA [] and PLLaVA [] for zero-shot evaluation and choose the model sizes as 7B. For evaluation, we use a GPT model to justify the correctness of free-form answers. Moreover, we finetune a pretrained InternVideo [] model that predicts answers with a classifier head.

## Peer Reviews

confidence: 2
contribution: 3
initial_rating: 6
presentation: 3
questions: 1. Have the authors considered extending the dataset to include articulated or deformable objects? If so, what challenges or limitations do they anticipate with this extension?

2. Could the authors provide an efficiency analysis of the proposed model, including resource usage and runtime under typical conditions?

3. What modifications to the NS-4DPhysics framework would make it more efficient for real-time performance or deployment in resource-limited environments?
soundness: 3
strengths: **1. Novel Dataset:** DynSuperCLEVR is a novel dataset that focuses on 4D dynamics, addressing a critical gap in existing VideoQA datasets which typically overlook explicit physics-based scene understanding.

**2. Innovative Model Design:** The NS-4DPhysics model combines 3D generative modeling with physics-informed priors, represents an innovative approach to handling dynamic 4D scene reasoning.

**3. Comprehensive Benchmarking:** Extensive evaluations against baseline models, including video large language models (Video-LLMs) and other symbolic frameworks, highlight the superior performance of NS-4DPhysics in capturing 4D dynamics.

**4. Future and Counterfactual Simulations:** By leveraging physics-based priors, the model excels at simulating both future and hypothetical states, demonstrating practical value and broad application potential.
summary: The paper introduces DynSuperCLEVR, a video question answering (VideoQA) dataset that emphasizes understanding dynamic 3D object properties within 4D (3D + time) scenes. 
Additionally, the authors present NS-4DPhysics, a model that combines neural-symbolic reasoning with physics-based priors to analyze these dynamic properties. The model first constructs an explicit 4D scene representation using a 3D generative model, followed by neural-symbolic reasoning to answer questions. 
Experimental results demonstrate that NS-4DPhysics surpasses existing VideoQA models across various question types (factual, predictive, and counterfactual), underscoring its effectiveness in reasoning about object dynamics in complex, synthetic environments.
weaknesses: **1. Synthetic Data Limitations:** While the dataset is suitable for testing dynamic properties, its synthetic nature may limit generalizability to real-world applications. Despite the authors’ efforts to improve aspects like background realism (L201), models trained exclusively on synthetic data often struggle to handle real-world noise and variability.

**2. Computational Complexity:** The NS-4DPhysics model is computationally demanding due to its reliance on 3D generative modeling and physics-based priors, presenting challenges for scalability and use in resource-constrained environments.

**3. Limited Object Diversity:** The dataset is limited to a narrow range of rigid objects, which may not adequately represent the complexity of real-world scenes that often include deformable or articulated objects.

**4. Evaluation of Real-World Applicability:** The paper lacks an analysis of the model’s performance on real-world video data, which is essential for evaluating its practical applicability outside synthetic benchmarks.

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: - It would be great if the author analyze the performance of proposed model on more complex dynamic scenarios such as non-rigid object deformation or fluid dynamics
- How do the authors plan to address the challenges in real datasets such as motion blur, camera shake, and varying lighting conditions?
- How do architectural choices  or important hyperparameters such as different CNN backbones or the choice of physics engine parameters impact on the performance of proposed model?
soundness: 3
strengths: - The paper's main objective of addressing multimodal 4D dynamics understanding is well-motivated.
- The authors provide comprehensive evaluation results across three types of reasoning tasks (factual, predictive, and counterfactual), demonstrating the model's capabilities in different scenarios.
- The proposed physics-aware neural-symbolic architecture presents an innovative approach
summary: This paper makes two main contributions: (1) introducing DynSuperCLEVR, a novel video question answering dataset that focuses on understanding 4D dynamics (velocity, acceleration, collisions) of objects in 3D scenes, and (2) proposing NS-4DPhysics, a neural-symbolic model that integrates physics priors with 3D scene understanding for dynamics reasoning. Through extensive experiments, their model significantly outperforms existing approaches, including large multimodal models, demonstrating current limitations in physical reasoning capabilities of video-language models.
weaknesses: - The dataset only considers rigid objects with linear velocity and acceleration. Real-world scenarios often involve more complex dynamics like non-rigid deformation, rotation-based motion, and fluid dynamics.
- The dataset uses synthetic rendering which may not capture real-world challenges like motion blur, camera shake, varying lighting conditions, and partial occlusions.
- The ablation studies are limited. While the paper shows the importance of physics priors, there could be more detailed analysis of other architectural choices and hyperparameters, like the impact of different CNN backbones or the choice of physics engine parameters.

---

confidence: 3
contribution: 3
initial_rating: 6
presentation: 3
questions: - How did you input the video frames to GPT-4o?
soundness: 2
strengths: - Innovative Dataset: DynSuperCLEVR fills a gap in VideoQA with a focus on 4D dynamics, including velocity, acceleration, and collision, enhancing video-based physics reasoning. The scene is programmed in a way that the ground truth information of object speeds and collision events can be documented and transformed into question-answer pairs. 
- Effective Model Design: NS-4DPhysics uses a physics-informed 4D scene representation and neural-symbolic reasoning, excelling in complex VideoQA tasks over baseline models.
- Comprehensive experiments demonstrate NS-4DPhysics’s superior performance in factual, predictive, and counterfactual reasoning.
summary: The paper addresses the dynamic properties of 3D objects in videos in the task of video question answering. It first proposes a new dataset called DynSuperCLEVR that composes multiple transportation objects into a scene and generates videos of these objects moving. The considered properties are speed, acceleration, and collision. Three types of questions are designed to test VLM's ability to understand the 3D dynamics of objects in these videos. A neural symbolic method, NS-4DPhysics, is proposed to address the importance of explicit 4D representation.
weaknesses: - The proposed dataset only spans a narrow domain of scenes and objects and may not generalize well to open-domain scenarios. The CLEVR-like setting makes things look nice and clean; for example, the objects have uniform colors, noise-free textures, rigid objects, and much fewer high-frequency details compared to realistic videos. Method comparison on the dataset may not reflect the true ability of the method in real-world videos.
- The reliance on physics priors might reduce flexibility in scenarios where these priors don’t apply as expected.

---

confidence: 3
contribution: 3
initial_rating: 6
presentation: 2
questions: See weaknesses.
soundness: 3
strengths: 1. Overall, the paper is easy-to-follow.
2. The motivation of the dataset is clear.
3. Experiment verifies the effectiveness on the proposed dataset.
summary: This work focuses on the understanding of the dynamic properties of 3D objects in videos. It uses a simulator to control physical concepts (velocity, acceleration, collisions, etc.)  to generate videos, and uses pre-defined programs to generate annotations. Based on the proposed dataset, this work introduces a physics prior based VQA model. The experiment shows its effectiveness on the proposed dataset.
weaknesses: 1. The content of the penultimate paragraph is confusing. Firstly, the motivation of the model is unclear. Why an explicit scene representation should be introduced to answer 4D dynamics questions? Secondly, what exactly is the"explicit scene representation"? Thirdly, what is the relation between the "scene parsing module" and "3D generative model", and how do the "3D generative representation" and "symbolic reasoning module" work together?
2. The significance of the proposed dataset is doubtful. In the reviewer's opinion, the scenarios in the proposed dataset may be too limited and fall far short of covering all real-world situations.
3. The effectiveness of the proposed model is also doubtful. The modules are specifically designed based on the actually unknown priors, i.e., the dataset generation and annotation processes. The authors should conduct experiments on spatiotemporal questions of other datasets, including both synthetic and real datasets.

Meta-review: This work proposes a new VideoQA dataset that emphasizes understanding dynamic 3D object properties within 4D. All reviewers consistently recommended accepting this work. AC agrees that this work is interesting and deserves to be published on ICLR 2O25. The reviewers did raise some valuable concerns that should be addressed in the final camera-ready version of the paper. The authors are encouraged to make the necessary changes in the final version.

Decision: Accept (Poster)