{"id":12988,"date":"2026-04-16T13:46:21","date_gmt":"2026-04-16T05:46:21","guid":{"rendered":"https:\/\/www.style3d.com\/blog\/?p=12988"},"modified":"2026-04-17T14:23:20","modified_gmt":"2026-04-17T06:23:20","slug":"synreal-decode-02-why-does-deformable-object-simulation-determine-the-upper-limit-of-physical-ai","status":"publish","type":"post","link":"https:\/\/www.style3d.com\/blog\/synreal-decode-02-why-does-deformable-object-simulation-determine-the-upper-limit-of-physical-ai\/","title":{"rendered":"SynReal Decode 02: Why Does “Deformable Object Simulation” Determine The Upper Limit of Physical AI?"},"content":{"rendered":"
In the previous article<\/a>, we established that:<\/span> simulation is a key source of data for AI.
\n<\/span>
\nBut this raises an important question:<\/span> what kind of simulation is truly valuable?<\/p>\n

<\/span><\/span><\/span>The answer is simple: not all simulations are created equal.<\/span><\/p>\n

In this installment of <\/span>SynReal Decode<\/em>, we continue this discussion: <\/span>if simulation is the gateway for AI to enter the real world, then what makes a simulation truly effective?<\/strong> When AI begins to engage with the real world, it quickly becomes clear that simulating a world that merely looks realistic is not difficult. However, simulating a world that behaves correctly is far more challenging. This difficulty increases dramatically when objects transition from rigid bodies to deformable materials such as fabrics and soft objects.<\/span><\/p>\n

In this article, we unpack <\/span>why deformable object simulation is emerging as a key dividing line in the development of physical AI.<\/strong>
\n<\/span><\/p>\n

https:\/\/help-ai.style3d.com\/wp-content\/uploads\/2026\/04\/Physics-on-off.mp4<\/a><\/video><\/div>\n

\u00a0<\/span><\/p>\n

 <\/p>\n

 <\/p>\n

1. Three Levels of Simulation<\/span><\/h2>\n

From a technical perspective, simulation is not a single concept but can be divided into different levels. In current AI and robotics research, it can be broadly categorized into three layers, each increasing in both difficulty and value.<\/span><\/p>\n

Level 1: 3D Environment and Space Simulation<\/span><\/h5>\n

The first level focuses on <\/span>simulating the three-dimensional structure of the physical world.<\/strong> The core objective of this level is to reconstruct the <\/span>spatial structure<\/strong> and <\/span>three-dimensional geometric relationships<\/strong> of the real world, enabling AI systems to develop fundamental 3D perception and spatial understanding, and thereby make reasonable decisions within their environment.<\/span><\/p>\n

For example, video generation models often produce inconsistencies in object size or spatial relationships. This stems from the model’s lack of a genuine understanding of three-dimensional spatial structure. By training models within virtual 3D environments\u2014where they can observe, reason, and learn\u2014these issues can be significantly reduced, allowing models to develop a more stable understanding of spatial perceptions.<\/span><\/p>\n

In robot navigation and autonomous driving, virtual cities, roads, and indoor environments enable AI systems to learn localization, obstacle avoidance, and path planning. Once AI acquires an understanding of three-dimensional space, it can be characterized as exhibiting “spatial intelligence.”<\/span><\/p>\n

Level 2: Rigid Body Physical Simulation<\/span><\/h5>\n

The second level is the <\/span>physical simulation of rigid objects<\/strong>.<\/span> While the first level deals primarily with static environments, this level focuses on <\/span>how interactions affect object states and positions. <\/strong><\/p>\n

In reality, <\/span>robots themselves are composed of rigid components connected by joints. Walking, climbing, and balancing all involve physical interactions with rigid surfaces such as the ground.<\/span> The ability of modern robots to traverse complex terrain is largely due to <\/span>the success of combining rigid body simulation with reinforcement learning.<\/strong><\/p>\n

Similarly, robotic arms can be trained to grasp objects such as cups or smartphones using rigid body simulation. This level of simulation is relatively mature and has already seen widespread adoption in both academia and industry.<\/span><\/p>\n

Level 3: Deformable Object and Complex Physics Simulation<\/span><\/h5>\n

While the first two levels have achieved significant progress, the third level presents far greater challenges, and remains a major focus of current research.<\/span><\/p>\n

This level involves <\/span>simulating deformable objects and complex physical phenomena such as fluids.<\/strong> In the real world, most objects are not rigid, from clothing, curtains, and sheets to plastic bags, packaging materials, plants, and even water and air. Strictly speaking, fluids do not have a fixed shape and can be regarded as an extreme form of deformable objects.
\n<\/span><\/p>\n

https:\/\/help-ai.style3d.com\/wp-content\/uploads\/2026\/04\/41.mp4<\/a><\/video><\/div>\n

\u00a0<\/span><\/p>\n

Unlike rigid objects, deformable objects do not have a single stable form. Their shape continuously changes depending on forces, environment, and contact conditions. This makes them significantly more difficult for AI systems to perceive, understand, and manipulate.<\/span><\/p>\n

This challenge is evident in real-world AI systems. Take cloth folding as an example: although robots today can perform this task, most rely heavily on large volumes of real-world demonstration data through imitation learning. In essence, these systems generalize within a limited range based on thousands of human demonstrations. Once faced with more complex garment styles, or even a change in fabric material, the original model may fail to function properly.<\/span><\/p>\n

From Simulation to Physical Intelligence<\/span><\/h5>\n

We believe that only by enabling AI to <\/span>truly understand the physical world and its underlying laws<\/strong>, can it interact reliably within complex environments, thereby achieving higher levels of intelligence. Only AI with such capabilities can be considered as <\/span>physical AI<\/strong>.<\/span><\/p>\n

In this process, <\/span>deformable object simulation <\/strong>plays a crucial role. It represents <\/span>the highest level of simulation and data synthesis capability<\/strong>, directly determining how complex and realistic the physical behaviors AI can learn, and ultimately defining <\/span>the upper limit of physical AI.<\/strong><\/p>\n

 <\/p>\n

 <\/p>\n

2.<\/span> Where Does Deformable Object Simulation Come From?<\/span><\/h2>\n

If deformable object simulation is so important, <\/span>where does it originate?<\/span><\/p>\n

It is not a standalone invention, but rather the result of the convergence of two major research domains.<\/span><\/p>\n

Source 1: Computational Physics and Engineering Simulation<\/span><\/h5>\n

The first source lies in the <\/span>numerical simulation technology<\/strong> widely used in traditional engineering disciplines, such as computational physics and mechanical engineering.<\/span> These methods are primarily used to simulate deformation under force, helping evaluate structural safety, material reliability, and design feasibility. <\/span><\/p>\n

As a result, <\/span>numerical accuracy is the top priority.<\/strong> Simulation outputs such as stress, deformation, and stability often directly determine the success or failure of engineering outcomes, leaving little room for error.<\/span><\/p>\n

However, from the perspective of artificial intelligence and data synthesis, these types of simulation also exhibit notable limitations. First, they typically do not prioritize visual realism, and <\/span>often present results in a simplified form.<\/strong> For example, in engineering mechanics, analyses are frequently conducted on two-dimensional cross-sections rather than fully simulating three-dimensional appearance.<\/span> Second, such simulations often <\/span>do not adequately handle complex contact and collision interactions.<\/strong> More importantly, engineering simulation <\/strong><\/span>is not typically designed with throughput as a primary objective, resulting in high computational costs and relatively slow performance.<\/strong><\/p>\n

For data synthesis, however, <\/span>the core advantage of simulation lies precisely in its cost-effectiveness. <\/span>If a single simulation requires substantial computational resources and time, it becomes difficult to support large-scale data generation.<\/span><\/p>\n

Source 2: Computer Graphics<\/span><\/h5>\n

The second source is computer graphics, originally developed for film, animation, and gaming. The realistic cloth, hair, smoke, and fluid effects we see in films today are all made possible by physical simulation techniques in computer graphics.<\/span><\/p>\n

The defining feature of this approach is its <\/span>emphasis on visual realism.<\/strong> As long as the result looks convincing, some simplifications in physical accuracy are acceptable. This aligns well with AI training needs, where perceptual realism often matters more than engineering-grade precision.<\/span><\/p>\n

On the other hand, computer graphics has long been constrained by performance limitations. <\/span>Achieving the highest possible level of realism under limited computational resources while reducing cost and improving efficiency<\/strong> has been a central challenge in the field.<\/span><\/p>\n

An extreme yet classic example is the video game, where real-time physics must run under strict computational limits, which forces researchers to continuously balance simulation fidelity against computational cost. Many efficient simulation methods emerged from this context. For instance, NVIDIA\u2019s PhysX engine\u2014widely used in robotics simulation today\u2014was originally designed for real-time physics in games.<\/span><\/p>\n

The Convergence of Two Paths<\/span><\/h5>\n

It’s important to know that these two sources are not isolated, instead, they have continuously influenced each other. From a practical application perspective, physical simulation techniques developed in computer graphics align closely with the needs of AI for simulation and data synthesis.<\/span><\/p>\n

More broadly, computer graphics and computer vision\u2014an important branch of AI focused on understanding images and video\u2014can be seen as two sides of the same coin: on one hand, computer graphics is dedicated to constructing virtual worlds and generating images and video; on the other, computer vision seeks to understand the real world from images and video.<\/span><\/p>\n

At the intersection of these fields, deformable object simulation has emerged as a key bridge between virtual and physical reality, forming essential infrastructure for physical AI.<\/strong><\/p>\n

 <\/p>\n

 <\/p>\n

3. The Challenges of Deformable Object Simulation<\/span><\/h2>\n

If deformable object simulation is so critical, why does it remain so difficult?<\/span><\/p>\n

In practice, the core challenges can be summarized in two words: <\/span>accuracy<\/strong> and <\/span>speed<\/strong>.<\/span><\/p>\n

Challenge 1: Accuracy<\/span><\/h5>\n

Simulation itself aims to replicate the real world as closely as possible. <\/span>The gap between simulation and reality, commonly referred to as the Sim-to-Real Gap, is a primary challenge.<\/strong><\/p>\n

Real-world materials exhibit complex properties. Differences in fabric type, thickness, elasticity, and friction significantly affect the motion and appearance of deformable objects. These properties are difficult to measure and even harder to model precisely. In many cases, simulation can only approximate behavior at the model level, inevitably introducing discrepancies with the real world.<\/span><\/p>\n

On the other hand, deformable objects are often accompanied by a<\/strong><\/span> large number of complex contact and friction relationships. <\/strong>These interactions continuously appear and disappear as shapes change. For example, during cloth folding, the fabric comes into contact not only with the table but also forms multiple layers of self-contact.<\/span> Handling such dynamic, multi-point, multi-layer contact without instability or penetration remains one of the most difficult problems in simulation. Even in rigid body simulation, contact handling is already highly complex; in deformable scenarios, this complexity is further amplified.<\/span><\/p>\n

Challenge 2: Speed<\/span><\/h5>\n

The second challenge is <\/span>efficiency. <\/strong>In film visual effects, it is indeed possible to achieve highly realistic results, but often without regard for cost. However, once simulation is applied to large-scale data synthesis, efficiency becomes an unavoidable core challenge. Only when simulation runs sufficiently fast can data be generated at scale.<\/span><\/p>\n

Against this backdrop, efficiency-related challenges begin to emerge. <\/span>A common trade-off is sacrificing accuracy for speed<\/strong>: using coarser time steps or simplified models. While this improves performance, it reduces physical realism.<\/span><\/p>\n

For example, NVIDIA PhysX, based on Position-Based Dynamics (PBD), is highly efficient for small-scale problems and widely used in real-time applications such as video games. However, it has inherent limitations in physical accuracy, and its performance degrades as problem complexity increases.<\/span><\/p>\n

Balancing Accuracy and Efficiency<\/span><\/h5>\n

Ultimately, accuracy and efficiency are fundamentally in tension.<\/span> The evolution of deformable object simulation is essentially a continuous search for balance between the two. <\/strong><\/p>\n

For a long time, computer graphics has been dedicated to achieving a better trade-off between realism and efficiency. This balance continues to evolve alongside advances in computational power. Over the past decade, the rapid development of parallel computing technologies, particularly GPUs, has significantly accelerated deformable simulation in terms of both speed and scale. Designing simulation algorithms that better adapt to parallel hardware such as GPUs, and thereby achieve higher data throughput, has become an important research direction in deformable object simulation.<\/span><\/p>\n

 <\/p>\n

 <\/p>\n

4. Our Work: Toward Scalable Deformable Object Simulation for Physical AI<\/span><\/h2>\n

In the preceding sections, we discussed why deformable object simulation is important and analyzed why it is so challenging. Against this backdrop, the work of the three of us\u2014Huamin Wang, Fanfu Jiangchen, and Yin Yang\u2014aims to address a concrete and practical question:<\/span> can we build a deformable object simulation system that is sufficiently accurate, sufficiently efficient, while also being able to scale reliably, and truly serve the training and learning needs of artificial intelligence?<\/strong><\/p>\n

Our research spans computer graphics, physical simulation, and AI, converging toward a unified goal: <\/span>transforming deformable object simulation from a research prototype into a foundational infrastructure for physical AI.<\/strong><\/p>\n

Accuracy: Physical Accuracy from Models to Materials (Huamin Wang)<\/span><\/h5>\n

The realism of deformable object simulation <\/span>depends largely on the underlying materials<\/strong>.<\/strong> Different fabrics, rubbers, and soft materials exhibit vastly different behaviors in bending, stretching, and friction. If the material model deviates significantly from the real world, then even a numerically stable simulation will struggle to provide truly valuable training data for artificial intelligence.
\n<\/span><\/p>\n