Search Results for author:
Found 13 papers, 7 papers with code
PoseBERT: A Generic Transformer Module for Temporal 3D Human Modeling
It is simple, generic and versatile, as it can be plugged on top of any image-based model to transform it in a video-based model leveraging temporal information.
Recovering Detail in 3D Shapes Using Disparity Maps
We present a fine-tuning method to improve the appearance of 3D geometries reconstructed from single images.
Learning Joint Surface Atlases
We demonstrate that this improves the quality of the learned surface representation, as well as its consistency in a collection of related shapes.
Neural Jacobian Fields: Learning Intrinsic Mappings of Arbitrary Meshes
This paper introduces a framework designed to accurately predict piecewise linear mappings of arbitrary meshes via a neural network, enabling training and evaluating over heterogeneous collections of meshes that do not share a triangulation, as well as producing highly detail-preserving maps whose accuracy exceeds current state of the art.
Leveraging MoCap Data for Human Mesh Recovery
In fact, we show that simply fine-tuning the batch normalization layers of the model is enough to achieve large gains.
Ranked #3 on
3D Human Pose Estimation
on MPI-INF-3DHP
(Acceleration Error metric)
Deep Transformation-Invariant Clustering
In contrast, we present an orthogonal approach that does not rely on abstract features but instead learns to predict image transformations and performs clustering directly in image space.
Ranked #2 on
Unsupervised Image Classification
on SVHN
(using extra training data)
Learning elementary structures for 3D shape generation and matching
We propose to represent shapes as the deformation and combination of learnable elementary 3D structures, which are primitives resulting from training over a collection of shape.
Ranked #5 on
3D Dense Shape Correspondence
on SHREC'19
(using extra training data)
Unsupervised cycle-consistent deformation for shape matching
We propose a self-supervised approach to deep surface deformation.
A Summary of the 4th International Workshop on Recovering 6D Object Pose
The workshop featured four invited talks, oral and poster presentations of accepted workshop papers, and an introduction of the BOP benchmark for 6D object pose estimation.
3D-CODED: 3D Correspondences by Deep Deformation
By predicting this feature for a new shape, we implicitly predict correspondences between this shape and the template.
3D-CODED : 3D Correspondences by Deep Deformation
By predicting this feature for a new shape, we implicitly predict correspondences between this shape and the template.
Ranked #6 on
3D Dense Shape Correspondence
on SHREC'19
(using extra training data)
A Papier-Mâché Approach to Learning 3D Surface Generation
We introduce a method for learning to generate the surface of 3D shapes.
AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation
We introduce a method for learning to generate the surface of 3D shapes.
Ranked #3 on
Point Cloud Completion
on Completion3D
