PhysForge: Material-Controllable 3D Reconstruction with Separable Components

Key points

• Recent advances in physical 3D generation have enabled visually faithful assets with simulatable mechanical behavior.1
• However, existing methods provide limited control over material behavior beyond appearance-based inference and struggle to estimate physical properties from complex interactions between objects and semantic parts.1
• The first, PhysFit, leverages material-conditioned VDM dynamics as references for physical-property fitting, introducing windowed fitting to focus on material-revealing interactions and Hallucinated Forcing to disentangle nonphysical motion hallucinated by the VDM from material responses.1
• Experiments on PhysAssets and Google Scanned Objects demonstrate that, compared with existing methods, PhysForge provides stronger control over material-dependent physical responses while supporting component-level physical modeling for complex interactions.1

Sources (1)

• [1]PhysForge: Material-Controllable 3D Reconstruction with Separable Components
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 02:47 PM

  Therefore, we present PhysForge, a Video Diffusion Model (VDM)-based framework for addressing these challenges, comprising two complementary modules for material-controllable physical fitting and componentlevel interaction modeling.

  Recent advances in physical 3D generation have enabled visually faithful assets with simulatable mechanical behavior.

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