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SOPHY: Learning to Generate Simulation-Ready Objects with Physical Materials

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arxiv 2504.12684 v3 pith:3ZVTAOKD submitted 2025-04-17 cs.GR cs.CV

classification cs.GRcs.CV
keywords materialobjectsgenerativephysicalshapegeneratedgeometryinteractive
verification ladder T0 review T1 audit T2 compute T3 formal
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We present SOPHY, a generative model for 3D physics-aware shape synthesis. Unlike existing 3D generative models that focus solely on static geometry or 4D models that produce physics-agnostic animations, our method jointly synthesizes shape, texture, and material properties related to physics-grounded dynamics, making the generated objects ready for simulations and interactive, dynamic environments. To train our model, we introduce a dataset of 3D objects annotated with detailed physical material attributes, along with an efficient pipeline for material annotation. Our method enables applications such as text-driven generation of interactive, physics-aware 3D objects and single-image reconstruction of physically plausible shapes. Furthermore, our experiments show that jointly modeling shape and material properties enhances the realism and fidelity of the generated shapes, improving performance on both generative geometry and physical plausibility.

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

  1. Vision Meets WiFi: Physics-Grounded Estimation of Volumetric Mechanical Properties

    cs.CV 2026-08 reject novelty 6.0 of 10

    ViWi uses material slots and a simulated RF descriptor to predict voxel-level Young's modulus, Poisson's ratio, and density, reporting gains over prior work on a synthetic benchmark.

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