22 citations · 42 across the 12 of their papers we have counts for
13 papers
Gradient Distance Function
Hieu Le, Federico Stella, Benoit Guillard +1
Unsigned Distance Functions (UDFs) can be used to represent non-watertight surfaces in a deep learning framework. However, UDFs tend to be brittle and difficult to learn, in part b…
A Latent Implicit 3D Shape Model for Multiple Levels of Detail
Benoit Guillard, Marc Habermann, Christian Theobalt +1
Implicit neural representations map a shape-specific latent code and a 3D coordinate to its corresponding signed distance (SDF) value. However, this approach only offers a single l…
Garment Recovery with Shape and Deformation Priors
Ren Li, Corentin Dumery, Benoît Guillard +1
While modeling people wearing tight-fitting clothing has made great strides in recent years, loose-fitting clothing remains a challenge. We propose a method that delivers realistic…
ISP: Multi-Layered Garment Draping with Implicit Sewing Patterns
Ren Li, Benoît Guillard, Pascal Fua
Many approaches to draping individual garments on human body models are realistic, fast, and yield outputs that are differentiable with respect to the body shape on which they are…
DrapeNet: Garment Generation and Self-Supervised Draping
Luca De Luigi, Ren Li, Benoît Guillard +2
Recent approaches to drape garments quickly over arbitrary human bodies leverage self-supervision to eliminate the need for large training sets. However, they are designed to train…
DIG: Draping Implicit Garment over the Human Body
Ren Li, Benoît Guillard, Edoardo Remelli +1
Existing data-driven methods for draping garments over human bodies, despite being effective, cannot handle garments of arbitrary topology and are typically not end-to-end differen…