Search Results for author:
Found 23 papers, 12 papers with code
AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation
We introduce a method for learning to generate the surface of 3D shapes.
Ranked #5 on
Point Cloud Completion
on Completion3D
A Papier-Mâché Approach to Learning 3D Surface Generation
We introduce a method for learning to generate the surface of 3D shapes.
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 #9 on
3D Dense Shape Correspondence
on SHREC'19
(using extra training data)
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.
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.
Unsupervised cycle-consistent deformation for shape matching
We propose a self-supervised approach to deep surface deformation.
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 #8 on
3D Dense Shape Correspondence
on SHREC'19
(using extra training data)
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)
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 #60 on
3D Human Pose Estimation
on 3DPW
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.
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.
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.
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.
NOPE: Novel Object Pose Estimation from a Single Image
The practicality of 3D object pose estimation remains limited for many applications due to the need for prior knowledge of a 3D model and a training period for new objects.
TextDeformer: Geometry Manipulation using Text Guidance
Our key observation is that Jacobians are a representation that favors smoother, large deformations, leading to a global relation between vertices and pixels, and avoiding localized noisy gradients.
Neural Face Rigging for Animating and Retargeting Facial Meshes in the Wild
We propose an end-to-end deep-learning approach for automatic rigging and retargeting of 3D models of human faces in the wild.
PSDR-Room: Single Photo to Scene using Differentiable Rendering
A 3D digital scene contains many components: lights, materials and geometries, interacting to reach the desired appearance.
CNOS: A Strong Baseline for CAD-based Novel Object Segmentation
We propose a simple three-stage approach to segment unseen objects in RGB images using their CAD models.
Generative Escher Meshes
We thus consider both the mesh's tile-shape and its texture as optimizable parameters, rendering the textured mesh via a differentiable renderer.
3DMiner: Discovering Shapes from Large-Scale Unannotated Image Datasets
We present 3DMiner -- a pipeline for mining 3D shapes from challenging large-scale unannotated image datasets.
GigaPose: Fast and Robust Novel Object Pose Estimation via One Correspondence
We present GigaPose, a fast, robust, and accurate method for CAD-based novel object pose estimation in RGB images.
Learning Continuous 3D Words for Text-to-Image Generation
We do this by engineering special sets of input tokens that can be transformed in a continuous manner -- we call them Continuous 3D Words.
MatAtlas: Text-driven Consistent Geometry Texturing and Material Assignment
We present MatAtlas, a method for consistent text-guided 3D model texturing.
