DiffCloth: Differentiable Cloth Simulation with Dry Frictional Contact

Abstract

Cloth simulation has wide applications in computer animation, garment design, and robot-assisted dressing. This work presents a differentiable cloth simulator whose additional gradient information facilitates cloth-related applications. Our differentiable simulator extends a state-of-the-art cloth simulator based on Projective Dynamics (PD) and with dry frictional contact. We draw inspiration from previous work to propose a fast and novel method for deriving gradients in PD-based cloth simulation with dry frictional contact. Furthermore, we conduct a comprehensive analysis and evaluation of the usefulness of gradients in contact-rich cloth simulation. Finally, we demonstrate the efficacy of our simulator in a number of downstream applications, including system identification, trajectory optimization for assisted dressing, closed-loop control, inverse design, and real-to-sim transfer. We observe a substantial speedup obtained from using our gradient information in solving most of these applications.

References in corpus (7)

• DiffTaichi: Differentiable Programming for Physical Simulation
• An End-to-End Differentiable Framework for Contact-Aware Robot Design
• gradSim: Differentiable simulation for system identification and visuomotor control
• Learning to Control PDEs with Differentiable Physics
• Scalable Differentiable Physics for Learning and Control
• ADD: Analytically Differentiable Dynamics for Multi-Body Systems with Frictional Contact
• P-Cloth: Interactive Complex Cloth Simulation on Multi-GPU Systems using Dynamic Matrix Assembly and Pipelined Implicit Integrators

Cited by in corpus (6)

• Differentiable solver for time-dependent deformation problems with contact
• Fluidic Topology Optimization with an Anisotropic Mixture Model
• How Will It Drape Like? Capturing Fabric Mechanics from Depth Images
• Neural Garment Dynamics via Manifold-Aware Transformers
• DiffSound: Differentiable Modal Sound Rendering and Inverse Rendering for Diverse Inference Tasks
• A Physics-embedded Deep Learning Framework for Cloth Simulation