• Corpus ID: 246441923

Fine-grained differentiable physics: a yarn-level model for fabrics

  title={Fine-grained differentiable physics: a yarn-level model for fabrics},
  author={Deshan Gong and Zhanxing Zhu and Andrew J. Bulpitt and He Wang},
Differentiable physics modeling combines physics models with gradient-based learning to provide model explicability and data efficiency. It has been used to learn dynamics, solve inverse problems and facilitate design, and is at its inception of impact. Current successes have concentrated on general physics models such as rigid bodies, deformable sheets, etc, assuming relatively simple structures and forces. Their granularity is intrinsically coarse and therefore incapable of modelling complex… 


Simulating knitted cloth at the yarn level
This work proposes an implicit-explicit integrator, with yarn inextensibility constraints imposed using efficient projections to simulate complex knitted garments, and shows that this simple model predicts the key mechanical properties of different knits, and can scale up to substantial animations with complex dynamic motion.
Data-driven elastic models for cloth: modeling and measurement
A piecewise linear elastic model is proposed that is a good approximation to nonlinear, anisotropic stretching and bending behaviors of various materials and can be fit to observed data with a well-posed optimization procedure.
DeepWrinkles: Accurate and Realistic Clothing Modeling
An entirely data-driven approach to realistic cloth wrinkle generation is claimed, which leads to unprecedented high-quality rendering of clothing deformation sequences, where fine wrinkles from (real) high resolution observations can be recovered.
Mixing Yarns and Triangles in Cloth Simulation
This paper proposes an enriched kinematic representation that augments triangle‐based deformations with yarn‐level details and introduces a preconditioner that resolves the poor conditioning produced by the extremely different inertia of triangle and yarn nodes.
PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics
A new differentiable physics benchmark called PasticineLab is introduced, which includes a diverse collection of soft body manipulation tasks, and experimental results suggest that RL-based approaches struggle to solve most of the tasks efficiently and gradient- based approaches can rapidly find a solution within tens of iterations, but still fall short on multi-stage tasks that require long-term planning.
Efficient yarn-based cloth with adaptive contact linearization
A method for approximating penalty-based contact forces in yarn-yarn collisions by computing the exact contact response at one time step, then using a rotated linear force model to approximate forces in nearby deformed configurations, enabling simulation of character-scale garments.
Homogenized yarn-level cloth
This work uses numerical homogenization to build a model of the potential energy density of the cloth, and uses this energy density function to compute forces in a thin shell simulator, which faithfully reproduces expected effects like the stiffness of woven fabrics, and the highly deformable nature and anisotropy of knitted fabrics.
A Bending Model for Nodal Discretizations of Yarn‐Level Cloth
This paper proposes a model of bending that is both robust and controllable, and employs only nodal degrees of freedom, and the computation of bending forces bears no overhead with respect to other nodal forces such as stretch.
Yarn-Level Cloth Simulation with Sliding Persistent Contacts
This work introduces a compact representation of yarn geometry and kinematics, capturing the essential deformation modes of yarn crossings, loops, stitches, and stacks, with a minimum cost, and designs force models that reproduce the characteristic macroscopic behavior of yarn-based fabrics.
gradSim: Differentiable simulation for system identification and visuomotor control
We consider the problem of estimating an object’s physical properties such as mass, friction, and elasticity directly from video sequences. Such a system identification problem is fundamentally