Core Concepts
Incorporating precise physics into text-to-3D generation methods enhances the practicality and realism of generated 3D shapes.
Abstract
The content introduces Phy3DGen, a method that bridges 3D virtual modeling with precise physics perception. It addresses the limitations of existing text-to-3D generation methods by focusing on geometric plausibility and physics accuracy. The paper proposes a two-stage approach involving a data-driven differentiable physics layer to optimize geometry efficiently while learning precise physics information. Experimental results demonstrate improved geometric plausibility and physical realism in generated 3D shapes.
Abstract:
- Existing text-to-3D methods lack precise physics perception.
- Phy3DGen bridges 3D virtual modeling with accurate physics information.
Introduction:
- Text-to-3D generation is crucial for various applications.
Motivation:
- Diffusion-SDF generates fragile geometries, highlighting the need for precise physics perception.
Method:
- Phy3DGen involves two stages: initialization with diffusion models and optimization with a differentiable physics layer.
Experiments:
- Comparison with Diffusion-SDF shows improved geometry quality.
Ablation Study:
- Removal of design loss affects geometry completeness.
Conclusion:
- Phy3DGen enhances 3D shape generation by considering both visual preferences and physical laws.
Quotes
"By incorporating precise physics into our method, our method needs to consider a more uniform stress distribution."
"Our method can generate higher-quality geometries, considering both visual realism and practical needs."