---
title: 'PhysX-Mobility: Simulation-Ready 3D Assets'
url: https://www.emergentmind.com/topics/physx-mobility
type: topic
---

# PhysX-Mobility: Simulation-Ready 3D Assets

PhysX-Mobility defines a large-scale dataset of richly annotated, simulation-ready 3D assets aimed at advancing physics-based simulation, embodied AI, and robotics research. The dataset substantially expands the semantic diversity and annotation richness beyond previous physical 3D benchmarks, offering over two thousand high-fidelity models with explicit geometry, physical properties, and articulated mechanisms, all optimized for direct integration with popular simulators such as MuJoCo. PhysX-Mobility is a cornerstone resource within the "PhysX-Anything" framework, facilitating simulation-ready asset generation and rigorous evaluation on contact-rich tasks [2511.13648].

## 1. Dataset Construction and Scale

PhysX-Mobility aggregates and extends previous repositories by applying rigorous cleaning, annotation, and synthetic augmentation:

- **Source models** include approximately 1,200 PartNet-Mobility scans and meshes, supplemented by 1,000 additional consumer-grade RGB-D scans. Each scan is paired with multiple real-world photographs for visual validation.
- **Annotation pipeline:** Automated manifold checks and manual mesh repair are performed. Rigid part segmentation is realized via voxel-based tagging. Material classes are assigned by database lookup; physical attributes (density, mass, inertia) are calculated analytically.
- **Articulation extraction:** Kinematic graphs are constructed from PartNet-Mobility, with manual axis and limit verification.
- **Synthetic augmentation:** Achieved via uniform random scaling (in [0.9, 1.1] for 20% of instances), friction coefficient jittering (±5%), and generation of 100 "hybrid" assemblies by swapping parts.
- **Scale and coverage:** The dataset comprises 2,184 unique 3D assets, expanding category coverage by over 2× relative to prior datasets. There are 47 semantic categories, including furniture (800 instances), office equipment (210), kitchen appliances (230), electronics (310), tools (180), household fixtures (240), and toys/miscellaneous items (214). Category sizes are maintained within ±20% variability.

## 2. Physical and Articulation Annotations

Each object decomposition features meticulous labeling of physical and mechanical properties:

- **Physical properties per rigid part:**
  - Mass $m = \rho V$, with $\rho$ the database-assigned density and $V$ part volume (from mesh).
  - Center of mass $\mathbf{c} \in \mathbb{R}^3$.
  - Inertia tensor $\mathbf{I} \in \mathbb{R}^{3 \times 3}$, diagonalized in the local frame.
  - Friction (static $\mu_s$, dynamic $\mu_d$) and restitution coefficient $e$.
- **Articulation semantics:**
  - Joint type $J \in \{\text{revolute}, \text{prismatic}, \text{ball}\}$.
  - Axis direction $\hat{\mathbf{u}} \in \mathbb{R}^3$, anchor $\mathbf{p} \in \mathbb{R}^3$.
  - Joint limits $[\theta_{\min}, \theta_{\max}]$ (revolute) or $[d_{\min}, d_{\max}]$ (prismatic).
  - Degrees of freedom: 1 for revolute/prismatic, 3 for ball joints.
  - For 15% of instances: Optional linear spring–damper with stiffness $k$ and damping $b$, and actuation torque limit $|\tau| \leq \tau_{\max}$.

## 3. 3D Representation, Tokenization, and Compression

PhysX-Mobility pioneers a compact, simulation-efficient 3D data structure:

- **Coarse geometry:** Occupancy grids on a $32^3$ voxel lattice ($\mathbf{V}^{\rm low} \in \{0,1\}^{32^3}$).
- **Sparse serialization:** Occupied voxel indices are linearized and merged into ranges, then serialized as a comma-separated string.
- **Compression:** This method achieves a $\sim$193× token count reduction relative to naïve mesh serialization (mean $\sim$26 tokens/asset), optimizing compatibility with vision-language model (VLM) context windows and efficient downstream processing.
- **Fine-grained decoding:** A flow-based transformer refines low-res voxels to $128^3$, supporting high-fidelity meshing for simulation use.

## 4. Data Format and Simulator Integration

Data is distributed for ready loading into major physics engines without manual post-processing:

- **Organization and schemas:**
  - `model.urdf` for kinematic and physical specification.
  - `model.sdf` for MuJoCo-style environments.
  - Per-part mesh (PLY/OBJ), texture (PNG/JPEG), and a `metadata.json` summary.
- **Simulator integration:** All physical and articulation properties, including joint limits, damping, friction, and inertia, are encoded natively in URDF/SDF and accessible via standard APIs (e.g., `mujoco_py`).
- **Example pipeline:** A MuJoCo-compatible object can be loaded and rendered interactively with a few lines of Python, with all physical parameters faithfully preserved from annotation to runtime.

## 5. Dataset Statistics and Category Distribution

Summary measures of PhysX-Mobility’s scale and diversity include:

| Statistic                | Value/Range                                            |
|--------------------------|-------------------------------------------------------|
| Object count             | 2,184                                                 |
| Categories               | 47                                                    |
| Longest dim (mean, $\sigma$) | 0.45 m, 0.32 m                                 |
| Parts/object (mean, $\sigma$) | 3.1, 1.4                                      |
| Mass (mean, range)       | 3.2 kg (0.05–45 kg), $\sigma$ = 5.7 kg                |
| Static friction $\mu_s$ (mean, $\sigma$) | 0.42, 0.15                         |
| Dynamic friction $\mu_d$ (mean, $\sigma$) | 0.35, 0.12                        |
| Articulation complexity  | 1,428 fixed, 512 1-DOF, 244 multi-DOF objects         |

Category distribution examples:

| Group            | Example Objects                    | Count |
|------------------|------------------------------------|-------|
| Furniture        | Chair, table, sofa, bed            | 800   |
| Office Equipment | Stapler, monitor                   | 210   |
| Kitchen          | Coffee machine, toaster            | 230   |
| Electronics      | Camera, fan                        | 310   |
| Tools            | Screwdriver, drill                 | 180   |
| Fixtures         | Faucet, cabinet door               | 240   |
| Toys/Misc        | Toy car, puzzle box                | 214   |

## 6. Generative Benchmarks and Downstream Performance

Performance on generative and robotic manipulation tasks is established by direct comparison to state-of-the-art baselines:

- **Generative 3D asset benchmarks** (higher better for PSNR/F-Score/material/affordance/kinematic/description; lower better for Chamfer, scale error):

| Metric                     | PhysX-Anything | PhysXGen | Articulate-Anything | URDFormer |
|----------------------------|---------------|----------|---------------------|-----------|
| PSNR (↑)                   | 20.35         | 20.33    | 16.90               | 7.97      |
| Chamfer CD (↓)             | 14.43         | 14.55    | 17.01               | 48.44     |
| F-Score (↑)                | 77.50         | 76.30    | 67.35               | 43.81     |
| Abs. scale error (↓)       | 0.30          | 43.44    | —                   | —         |
| Material acc. (↑)          | 17.52         | 6.29     | —                   | —         |
| Affordance acc. (↑)        | 14.28         | 9.75     | —                   | —         |
| Kinematic param (↑)        | 0.94          | 0.71     | 0.65                | 0.31      |
| Description coher. (↑)     | 19.36         | 12.89    | —                   | —         |

- **Robotic policy learning:** In a MuJoCo-style simulation over 120 manipulation tasks (e.g., turning faucets, opening cabinets), policies trained on PhysX-Mobility assets achieved a 92% success rate and normalized reward of 0.78, compared to 61% and 0.45 for the PhysXGen baseline. This demonstrates not only generative fidelity but immediate usability for contact-rich, articulated interactions.

## 7. Context, Applications, and Impact

PhysX-Mobility enables simulation-ready, richly annotated physical assets for:

- **Embodied AI:** Facilitating generalization and policy learning in robotic manipulation, tool use, and interactive perception.
- **Computer vision and 3D generation:** Serving as a primary resource for VLM-based geometry-aware modeling, supporting advanced tokenization and context-efficient training protocols.
- **Physics-based simulation:** Providing a comprehensive testbed for benchmarking sim-to-real transfer, active safety evaluation, and articulated object control in environments including MuJoCo and other URDF/SDF-compatible engines.
- **Dataset design:** Demonstrating annotation, compression, and integration best practices, including explicit calculation of mass, inertia, and kinematic limits directly from mesh and physical database lookups.

*A plausible implication is* that PhysX-Mobility’s design—specifically its 193× geometry token compression, granularity of ground-truth physics metadata, and extensive semantic coverage—will serve as a reference standard for simulation-ready physical datasets in robotics and AI for the foreseeable future [2511.13648].

Source: https://www.emergentmind.com/topics/physx-mobility