Search Results for author:
Found 5 papers, 3 papers with code
SNUG: Self-Supervised Neural Dynamic Garments
We present a self-supervised method to learn dynamic 3D deformations of garments worn by parametric human bodies.
Self-Supervised Collision Handling via Generative 3D Garment Models for Virtual Try-On
We propose a new generative model for 3D garment deformations that enables us to learn, for the first time, a data-driven method for virtual try-on that effectively addresses garment-body collisions.
Fully Convolutional Graph Neural Networks for Parametric Virtual Try-On
Then, after a mesh topology optimization step where we generate a sufficient level of detail for the input garment type, we further deform the mesh to reproduce deformations caused by the target body shape.
SoftSMPL: Data-driven Modeling of Nonlinear Soft-tissue Dynamics for Parametric Humans
We present SoftSMPL, a learning-based method to model realistic soft-tissue dynamics as a function of body shape and motion.
Learning-Based Animation of Clothing for Virtual Try-On
We propose a model that separates global garment fit, due to body shape, from local garment wrinkles, due to both pose dynamics and body shape.
