Physically Compatible 3D Object Modeling from a Single Image

• URL: http://arxiv.org/abs/2405.20510v2
• Date: Mon, 3 Jun 2024 22:34:58 GMT
• Title: Physically Compatible 3D Object Modeling from a Single Image
• Authors: Minghao Guo, Bohan Wang, Pingchuan Ma, Tianyuan Zhang, Crystal Elaine Owens, Chuang Gan, Joshua B. Tenenbaum, Kaiming He, Wojciech Matusik,
• Abstract summary: We present a framework that transforms single images into 3D physical objects. Our framework embeds physical compatibility into the reconstruction process. It consistently enhances the physical realism of 3D models over existing methods.
• Score: 109.98124149566927
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

Related papers

• PhysMotion: Physics-Grounded Dynamics From a Single Image [24.096925413047217]
  We introduce PhysMotion, a novel framework that leverages principled physics-based simulations to guide intermediate 3D representations generated from a single image. Our approach addresses the limitations of traditional data-driven generative models and result in more consistent physically plausible motions.
  arXiv  Detail & Related papers  (2024-11-26T07:59:11Z)
• PhysPart: Physically Plausible Part Completion for Interactable Objects [28.91080122885566]
  We tackle the problem of physically plausible part completion for interactable objects. We propose a diffusion-based part generation model that utilizes geometric conditioning. We also demonstrate our applications in 3D printing, robot manipulation, and sequential part generation.
  arXiv  Detail & Related papers  (2024-08-25T04:56:09Z)
• Physics3D: Learning Physical Properties of 3D Gaussians via Video Diffusion [35.71595369663293]
  We propose textbfPhysics3D, a novel method for learning various physical properties of 3D objects through a video diffusion model. Our approach involves designing a highly generalizable physical simulation system based on a viscoelastic material model. Experiments demonstrate the effectiveness of our method with both elastic and plastic materials.
  arXiv  Detail & Related papers  (2024-06-06T17:59:47Z)
• Atlas3D: Physically Constrained Self-Supporting Text-to-3D for Simulation and Fabrication [50.541882834405946]
  We introduce Atlas3D, an automatic and easy-to-implement text-to-3D method. Our approach combines a novel differentiable simulation-based loss function with physically inspired regularization. We verify Atlas3D's efficacy through extensive generation tasks and validate the resulting 3D models in both simulated and real-world environments.
  arXiv  Detail & Related papers  (2024-05-28T18:33:18Z)
• PhyRecon: Physically Plausible Neural Scene Reconstruction [81.73129450090684]
  We introduce PHYRECON, the first approach to leverage both differentiable rendering and differentiable physics simulation to learn implicit surface representations. Central to this design is an efficient transformation between SDF-based implicit representations and explicit surface points. Our results also exhibit superior physical stability in physical simulators, with at least a 40% improvement across all datasets.
  arXiv  Detail & Related papers  (2024-04-25T15:06:58Z)
• Fixing Malfunctional Objects With Learned Physical Simulation and Functional Prediction [158.74130075865835]
  Given a malfunctional 3D object, humans can perform mental simulations to reason about its functionality and figure out how to fix it. To mimic humans' mental simulation process, we present FixNet, a novel framework that seamlessly incorporates perception and physical dynamics.
  arXiv  Detail & Related papers  (2022-05-05T17:59:36Z)
• {\phi}-SfT: Shape-from-Template with a Physics-Based Deformation Model [69.27632025495512]
  Shape-from-Template (SfT) methods estimate 3D surface deformations from a single monocular RGB camera. This paper proposes a new SfT approach explaining 2D observations through physical simulations.
  arXiv  Detail & Related papers  (2022-03-22T17:59:57Z)
• Learning Canonical 3D Object Representation for Fine-Grained Recognition [77.33501114409036]
  We propose a novel framework for fine-grained object recognition that learns to recover object variation in 3D space from a single image. We represent an object as a composition of 3D shape and its appearance, while eliminating the effect of camera viewpoint. By incorporating 3D shape and appearance jointly in a deep representation, our method learns the discriminative representation of the object.
  arXiv  Detail & Related papers  (2021-08-10T12:19:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.