We present a self-supervised method to learn dynamic 3D deformations of garments worn
by parametric human bodies. State-of-the-art data-driven approaches to model 3D garment …

We present a general framework for the garment animation problem through unsupervised
deep learning inspired in physically based simulation. Existing trends in the literature …

In this paper, we introduce the Moving Least Squares Material Point Method (MLS-MPM).
MLS-MPM naturally leads to the formulation of Affine Particle-In-Cell (APIC)[Jiang et al …

Recent progress in deep learning is essentially based on a “big data for small tasks”
paradigm, under which massive amounts of data are used to train a classifier for a single …

We extend the incremental potential contact (IPC) model for contacting elastodynamics to
resolve systems composed of codimensional DOFs in arbitrary combination. This enables a …

The paper gives an overview of Material Point Method and shows its evolution over the last
25 years. The Material Point Method developments followed a logical order. The article aims …

Simulating the physical behaviors of deformable objects and fluids has been an important
topic in computer graphics. While the Lagrangian Finite Element Method (FEM) is widely …

We present a new method for real-time physics-based simulation supporting many different
types of hyperelastic materials. Previous methods such as Position-Based or Projective …

We simulate sand dynamics using an elastoplastic, continuum assumption. We demonstrate
that the Drucker-Prager plastic flow model combined with a Hencky-strain-based …

Abstract Implicit Neural Spatial Representation (INSR) has emerged as an effective
representation of spatially-dependent vector fields. This work explores solving time …