Search Results for author:
Found 6 papers, 4 papers with code
NRDF: Neural Riemannian Distance Fields for Learning Articulated Pose Priors
Faithfully modeling the space of articulations is a crucial task that allows recovery and generation of realistic poses, and remains a notorious challenge.
CloSe: A 3D Clothing Segmentation Dataset and Model
Additionally, we propose CloSe-Net, the first learning-based 3D clothing segmentation model for fine-grained segmentation from colored point clouds.
Pose-NDF: Modeling Human Pose Manifolds with Neural Distance Fields
The resulting high-dimensional implicit function can be differentiated with respect to the input poses and thus can be used to project arbitrary poses onto the manifold by using gradient descent on the set of 3-dimensional hyperspheres.
Neural-GIF: Neural Generalized Implicit Functions for Animating People in Clothing
Neural-GIF can be trained on raw 3D scans and reconstructs detailed complex surface geometry and deformations.
SIZER: A Dataset and Model for Parsing 3D Clothing and Learning Size Sensitive 3D Clothing
SizerNet allows to estimate and visualize the dressing effect of a garment in various sizes, and ParserNet allows to edit clothing of an input mesh directly, removing the need for scan segmentation, which is a challenging problem in itself.
Multi-Garment Net: Learning to Dress 3D People from Images
We present Multi-Garment Network (MGN), a method to predict body shape and clothing, layered on top of the SMPL model from a few frames (1-8) of a video.
3D Human Pose Estimation
3D Shape Reconstruction From A Single 2D Image
