3 citations · 7 across the 5 of their papers we have counts for
5 papers · 1 filter
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…
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…
Sketch2Mesh: Reconstructing and Editing 3D Shapes from Sketches
Benoit Guillard, Edoardo Remelli, Pierre Yvernay +1
Reconstructing 3D shape from 2D sketches has long been an open problem because the sketches only provide very sparse and ambiguous information. In this paper, we use an encoder/dec…
UCLID-Net: Single View Reconstruction in Object Space
Benoit Guillard, Edoardo Remelli, Pascal Fua
Most state-of-the-art deep geometric learning single-view reconstruction approaches rely on encoder-decoder architectures that output either shape parametrizations or implicit repr…
MeshSDF: Differentiable Iso-Surface Extraction
Edoardo Remelli, Artem Lukoianov, Stephan R. Richter +4
Geometric Deep Learning has recently made striking progress with the advent of continuous Deep Implicit Fields. They allow for detailed modeling of watertight surfaces of arbitrary…