Search Results for author:
Found 32 papers, 10 papers with code
ANIM: Accurate Neural Implicit Model for Human Reconstruction from a single RGB-D image
In this paper, we explore the benefits of incorporating depth observations in the reconstruction process by introducing ANIM, a novel method that reconstructs arbitrary 3D human shapes from single-view RGB-D images with an unprecedented level of accuracy.
HISR: Hybrid Implicit Surface Representation for Photorealistic 3D Human Reconstruction
This representation is composed of two surface layers that represent opaque and translucent regions on the clothed human body.
Deformation-Guided Unsupervised Non-Rigid Shape Matching
Our approach is designed to be particularly robust when matching shapes digitized using 3D scanners that contain fine geometric detail and suffer from different types of noise including topological noise caused by the coalescence of spatially close surface regions.
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.
4DHumanOutfit: a multi-subject 4D dataset of human motion sequences in varying outfits exhibiting large displacements
This work presents 4DHumanOutfit, a new dataset of densely sampled spatio-temporal 4D human motion data of different actors, outfits and motions.
3DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challenge
To address these challenges, the 3DTeethSeg'22 challenge was organized in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2022, with a call for algorithms tackling teeth localization, segmentation, and labeling from intraoral 3D scans.
Human Body Shape Completion With Implicit Shape and Flow Learning
We adopt such a representation and explore how the motion flow between two consecutive frames can contribute to the shape completion task.
Teeth3DS: a benchmark for teeth segmentation and labeling from intra-oral 3D scans
Teeth segmentation and labeling are critical components of Computer-Aided Dentistry (CAD) systems.
Multi-View Reconstruction using Signed Ray Distance Functions (SRDF)
Our approach bridges the gap between the two strategies with a novel volumetric shape representation that is implicit but parameterized with pixel depths to better materialize the shape surface with consistent signed distances along viewing rays.
Vertebrae localization, segmentation and identification using a graph optimization and an anatomic consistency cycle
Vertebrae localization, segmentation and identification in CT images is key to numerous clinical applications.
3D Human Shape Style Transfer
Traditional solutions follow a pose transfer strategy, from the moving character to the source character shape, that relies on skeletal pose parametrization.
Neural Human Deformation Transfer
We consider the problem of human deformation transfer, where the goal is to retarget poses between different characters.
Data-Driven 3D Reconstruction of Dressed Humans From Sparse Views
We propose a data-driven end-to-end approach that reconstructs an implicit 3D representation of dressed humans from sparse camera views.
Dual Mesh Convolutional Networks for Human Shape Correspondence
Convolutional networks have been extremely successful for regular data structures such as 2D images and 3D voxel grids.
Mesh Denoising with Facet Graph Convolutions
Most existing mesh denoising methods still use hand-crafted features, and locally denoise facets rather than examine the mesh globally.
Articulated Shape Matching Using Laplacian Eigenfunctions and Unsupervised Point Registration
Matching articulated shapes represented by voxel-sets reduces to maximal sub-graph isomorphism when each set is described by a weighted graph.
Topology-Adaptive Mesh Deformation for Surface Evolution, Morphing, and Multi-View Reconstruction
However, explicit mesh representations, while allowing for accurate surface modelling, suffer from the inherent difficulty of reliably dealing with self-intersections and topological changes such as merges and splits.
Human Motion Tracking by Registering an Articulated Surface to 3-D Points and Normals
We address the problem of human motion tracking by registering a surface to 3-D data.
Discrete Point Flow Networks for Efficient Point Cloud Generation
Generative models have proven effective at modeling 3D shapes and their statistical variations.
Cross-modal Deep Face Normals with Deactivable Skip Connections
Core to our approach is a novel module that we call deactivable skip connections, which allows integrating both the auto-encoded and image-to-normal branches within the same architecture that can be trained end-to-end.
Probabilistic Reconstruction Networks for 3D Shape Inference from a Single Image
We study end-to-end learning strategies for 3D shape inference from images, in particular from a single image.
A Decoupled 3D Facial Shape Model by Adversarial Training
Data-driven generative 3D face models are used to compactly encode facial shape data into meaningful parametric representations.
Shape Reconstruction Using Volume Sweeping and Learned Photoconsistency
Our results demonstrate this ability, showing that a CNN, trained on a standard static dataset, can help recover surface details on dynamic scenes that are not perceived by traditional 2D feature based methods.
Multi-View Dynamic Shape Refinement Using Local Temporal Integration
We consider 4D shape reconstructions in multi-view environments and investigate how to exploit temporal redundancy for precision refinement.
Surface Motion Capture Transfer With Gaussian Process Regression
We address the problem of transferring motion between captured 4D models.
FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis
Convolutional neural networks (CNNs) have massively impacted visual recognition in 2D images, and are now ubiquitous in state-of-the-art approaches.
Volumetric 3D Tracking by Detection
In this paper, we propose a new framework for 3D tracking by detection based on fully volumetric representations.
Shape Animation with Combined Captured and Simulated Dynamics
Instead of using the dominant surface-based geometric representation of the capture, which is less suitable for volumetric effects, our pipeline exploits Centroidal Voronoi tessellation decompositions as unified volumetric representation of the real captured actor, which we show can be used seamlessly as a building block for all processing stages, from capture and tracking to virtual physic simulation.
Toward User-Specific Tracking by Detection of Human Shapes in Multi-Cameras
It usually comprises an association step, that finds correspondences between the model and the input data, and a deformation step, that fits the model to the observations given correspondences.
An Efficient Volumetric Framework for Shape Tracking
While numerically plausible, this paradigm ignores the fact that the observed surfaces often delimit volumetric shapes, for which deformations are constrained by the volume inside the shape.
High Resolution 3D Shape Texture from Multiple Videos
To this goal we use 2D warps for all viewpoints and all temporal frames and a linear image formation model from texture to image space.
Human Shape and Pose Tracking Using Keyframes
In contrast to many existing approaches that rely on a single reference model, multiple templates represent a larger variability of human poses.
