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Physically Compatible 3D Object Modeling from a Single Image

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arxiv 2405.20510 v3 pith:YC4LCLHV submitted 2024-05-30 cs.CV

classification cs.CV
keywords physicalframeworkforcesimageattributescompatibilityexistingexternal
verification ladder T0 review T1 audit T2 compute T3 formal
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We present a computational framework that transforms single images into 3D physical objects. The visual geometry of a physical object in an image is determined by three orthogonal attributes: mechanical properties, external forces, and rest-shape geometry. Existing single-view 3D reconstruction methods often overlook this underlying composition, presuming rigidity or neglecting external forces. Consequently, the reconstructed objects fail to withstand real-world physical forces, resulting in instability or undesirable deformation -- diverging from their intended designs as depicted in the image. Our optimization framework addresses this by embedding physical compatibility into the reconstruction process. We explicitly decompose the three physical attributes and link them through static equilibrium, which serves as a hard constraint, ensuring that the optimized physical shapes exhibit desired physical behaviors. Evaluations on a dataset collected from Objaverse demonstrate that our framework consistently enhances the physical realism of 3D models over existing methods. The utility of our framework extends to practical applications in dynamic simulations and 3D printing, where adherence to physical compatibility is paramount.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Pixie: Fast and Generalizable Supervised Learning of 3D Physics from Pixels

    cs.CV 2025-08 reject novelty 5.0 of 10

    A supervised 3D U-Net predicts per-voxel material fields from CLIP feature grids, enabling fast MPM-based animation, but the reported evidence depends on pseudo-labels and a VLM judge from the same model family as the...

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