PhysForge: Generating Physics-Grounded 3D Assets for Interactive Virtual World
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911653099995136 |
|---|---|
| author | Yang, Yunhan Wang, Chunshi Ye, Junliang Li, Yang Chen, Zanxin Huang, Zehuan Mu, Yao Chen, Zhuo Guo, Chunchao Liu, Xihui |
| author_facet | Yang, Yunhan Wang, Chunshi Ye, Junliang Li, Yang Chen, Zanxin Huang, Zehuan Mu, Yao Chen, Zhuo Guo, Chunchao Liu, Xihui |
| contents | Synthesizing physics-grounded 3D assets is a critical bottleneck for interactive virtual worlds and embodied AI. Existing methods predominantly focus on static geometry, overlooking the functional properties essential for interaction. We propose that interactive asset generation must be rooted in functional logic and hierarchical physics. To bridge this gap, we introduce PhysForge, a decoupled two-stage framework supported by PhysDB, a large-scale dataset of 150,000 assets with four-tier physical annotations. First, a VLM acts as a "physical architect" to plan a "Hierarchical Physical Blueprint" defining material, functional, and kinematic constraints. Second, a physics-grounded diffusion model realizes this blueprint by synthesizing high-fidelity geometry alongside precise kinematic parameters via a novel KineVoxel Injection (KVI) mechanism. Experiments demonstrate that PhysForge produces functionally plausible, simulation-ready assets, providing a robust data engine for interactive 3D content and embodied agents. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_05163 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PhysForge: Generating Physics-Grounded 3D Assets for Interactive Virtual World Yang, Yunhan Wang, Chunshi Ye, Junliang Li, Yang Chen, Zanxin Huang, Zehuan Mu, Yao Chen, Zhuo Guo, Chunchao Liu, Xihui Computer Vision and Pattern Recognition Synthesizing physics-grounded 3D assets is a critical bottleneck for interactive virtual worlds and embodied AI. Existing methods predominantly focus on static geometry, overlooking the functional properties essential for interaction. We propose that interactive asset generation must be rooted in functional logic and hierarchical physics. To bridge this gap, we introduce PhysForge, a decoupled two-stage framework supported by PhysDB, a large-scale dataset of 150,000 assets with four-tier physical annotations. First, a VLM acts as a "physical architect" to plan a "Hierarchical Physical Blueprint" defining material, functional, and kinematic constraints. Second, a physics-grounded diffusion model realizes this blueprint by synthesizing high-fidelity geometry alongside precise kinematic parameters via a novel KineVoxel Injection (KVI) mechanism. Experiments demonstrate that PhysForge produces functionally plausible, simulation-ready assets, providing a robust data engine for interactive 3D content and embodied agents. |
| title | PhysForge: Generating Physics-Grounded 3D Assets for Interactive Virtual World |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.05163 |