Cloth in the Wind: A Case Study of Physical Measurement through Simulation

• URL: http://arxiv.org/abs/2003.05065v1
• Date: Mon, 9 Mar 2020 21:32:23 GMT
• Title: Cloth in the Wind: A Case Study of Physical Measurement through Simulation
• Authors: Tom F.H. Runia, Kirill Gavrilyuk, Cees G.M. Snoek, Arnold W.M. Smeulders
• Abstract summary: We propose to measure latent physical properties for cloth in the wind without ever having seen a real example before. Our solution is an iterative refinement procedure with simulation at its core. The correspondence is measured using an embedding function that maps physically similar examples to nearby points.
• Score: 50.31424339972478
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For many of the physical phenomena around us, we have developed sophisticated models explaining their behavior. Nevertheless, measuring physical properties from visual observations is challenging due to the high number of causally underlying physical parameters -- including material properties and external forces. In this paper, we propose to measure latent physical properties for cloth in the wind without ever having seen a real example before. Our solution is an iterative refinement procedure with simulation at its core. The algorithm gradually updates the physical model parameters by running a simulation of the observed phenomenon and comparing the current simulation to a real-world observation. The correspondence is measured using an embedding function that maps physically similar examples to nearby points. We consider a case study of cloth in the wind, with curling flags as our leading example -- a seemingly simple phenomena but physically highly involved. Based on the physics of cloth and its visual manifestation, we propose an instantiation of the embedding function. For this mapping, modeled as a deep network, we introduce a spectral layer that decomposes a video volume into its temporal spectral power and corresponding frequencies. Our experiments demonstrate that the proposed method compares favorably to prior work on the task of measuring cloth material properties and external wind force from a real-world video.

Related papers

• Latent Intuitive Physics: Learning to Transfer Hidden Physics from A 3D Video [58.043569985784806]
  We introduce latent intuitive physics, a transfer learning framework for physics simulation. It can infer hidden properties of fluids from a single 3D video and simulate the observed fluid in novel scenes. We validate our model in three ways: (i) novel scene simulation with the learned visual-world physics, (ii) future prediction of the observed fluid dynamics, and (iii) supervised particle simulation.
  arXiv  Detail & Related papers  (2024-06-18T16:37:44Z)
• DiffAvatar: Simulation-Ready Garment Optimization with Differentiable Simulation [27.553678582454648]
  Physical simulations can produce realistic motions for clothed humans, but they require high-quality garment assets with associated physical parameters for cloth simulations. We propose papername,a novel approach that performs body and garment co-optimization using differentiable simulation. Our experiments demonstrate that our approach generates realistic clothing and body shape suitable for downstream applications.
  arXiv  Detail & Related papers  (2023-11-20T21:20:37Z)
• Physion++: Evaluating Physical Scene Understanding that Requires Online Inference of Different Physical Properties [100.19685489335828]
  This work proposes a novel dataset and benchmark, termed Physion++, to rigorously evaluate visual physical prediction in artificial systems. We test scenarios where accurate prediction relies on estimates of properties such as mass, friction, elasticity, and deformability. We evaluate the performance of a number of state-of-the-art prediction models that span a variety of levels of learning vs. built-in knowledge, and compare that performance to a set of human predictions.
  arXiv  Detail & Related papers  (2023-06-27T17:59:33Z)
• ComPhy: Compositional Physical Reasoning of Objects and Events from Videos [113.2646904729092]
  The compositionality between the visible and hidden properties poses unique challenges for AI models to reason from the physical world. Existing studies on video reasoning mainly focus on visually observable elements such as object appearance, movement, and contact interaction. We propose an oracle neural-symbolic framework named Compositional Physics Learner (CPL), combining visual perception, physical property learning, dynamic prediction, and symbolic execution.
  arXiv  Detail & Related papers  (2022-05-02T17:59:13Z)
• DiffSDFSim: Differentiable Rigid-Body Dynamics With Implicit Shapes [9.119424247289857]
  Differentiable physics is a powerful tool in computer and robotics for scene understanding and reasoning about interactions. Existing approaches have frequently been limited to objects with simple shape or shapes that are in advance.
  arXiv  Detail & Related papers  (2021-11-30T11:56:24Z)
• Visual Vibration Tomography: Estimating Interior Material Properties from Monocular Video [66.94502090429806]
  An object's interior material properties, while invisible to the human eye, determine motion observed on its surface. We propose an approach that estimates heterogeneous material properties of an object from a monocular video of its surface vibrations.
  arXiv  Detail & Related papers  (2021-04-06T18:05:27Z)
• Visual Grounding of Learned Physical Models [66.04898704928517]
  Humans intuitively recognize objects' physical properties and predict their motion, even when the objects are engaged in complicated interactions. We present a neural model that simultaneously reasons about physics and makes future predictions based on visual and dynamics priors. Experiments show that our model can infer the physical properties within a few observations, which allows the model to quickly adapt to unseen scenarios and make accurate predictions into the future.
  arXiv  Detail & Related papers  (2020-04-28T17:06:38Z)

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.