Search Results for author:
Found 29 papers, 11 papers with code
D-Garment: Physics-Conditioned Latent Diffusion for Dynamic Garment Deformations
Adjusting and deforming 3D garments to body shapes, body motion, and cloth material is an important problem in virtual and augmented reality.
Pose-independent 3D Anthropometry from Sparse Data
We show that our method achieves comparable results to competing methods that use dense geometry in the standard A-pose, but has the capability of estimating the body measurements from any pose using sparse landmarks only.
Combining Neural Fields and Deformation Models for Non-Rigid 3D Motion Reconstruction from Partial Data
We introduce a novel, data-driven approach for reconstructing temporally coherent 3D motion from unstructured and potentially partial observations of non-rigidly deforming shapes.
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.
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.
Correspondence-free online human motion retargeting
We present a data-driven framework for unsupervised human motion retargeting that animates a target subject with the motion of a source subject.
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.
Representing motion as a sequence of latent primitives, a flexible approach for human motion modelling
We propose a new representation of human body motion which encodes a full motion in a sequence of latent motion primitives.
Neural Human Deformation Transfer
We consider the problem of human deformation transfer, where the goal is to retarget poses between different characters.
A structured latent space for human body motion generation
We propose a framework to learn a structured latent space to represent 4D human body motion, where each latent vector encodes a full motion of the whole 3D human shape.
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.
3D Morphable Face Models -- Past, Present and Future
In this paper, we provide a detailed survey of 3D Morphable Face Models over the 20 years since they were first proposed.
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.
Analyzing Clothing Layer Deformation Statistics of 3D Human Motions
Recent capture technologies and methods allow not only to retrieve 3D model sequence of moving people in clothing, but also to separate and extract the underlying body geometry, motion component and the clothing as a geometric layer.
A Multilinear Tongue Model Derived from Speech Related MRI Data of the Human Vocal Tract
We present a multilinear statistical model of the human tongue that captures anatomical and tongue pose related shape variations separately.
A Robust Multilinear Model Learning Framework for 3D Faces
Multilinear models are widely used to represent the statistical variations of 3D human faces as they decouple shape changes due to identity and expression.
Fitting a 3D Morphable Model to Edges: A Comparison Between Hard and Soft Correspondences
We propose a fully automatic method for fitting a 3D morphable model to single face images in arbitrary pose and lighting.
A Groupwise Multilinear Correspondence Optimization for 3D Faces
To compute a high-quality multilinear face model, the quality of the registration of the database of 3D face scans used for training is essential.
A statistical shape space model of the palate surface trained on 3D MRI scans of the vocal tract
The palate model is then tested using 3D MRI from another corpus and evaluated using a high-resolution optical scan.
Building Statistical Shape Spaces for 3D Human Modeling
Statistical models of 3D human shape and pose learned from scan databases have developed into valuable tools to solve a variety of vision and graphics problems.
3D faces in motion: Fully automatic registration and statistical analysis
The resulting statistical analysis is applied to automatically generate realistic facial animations and to recognize dynamic facial expressions.
Review of Statistical Shape Spaces for 3D Data with ComparativeAnalysis for Human Faces
Due to the wide avail-ability of databases of high-quality data, we use the human face as the specific shape we wish to extract from corrupted data.
Multilinear Wavelets: A Statistical Shape Space for Human Faces
We show that in comparison to a global multilinear model, our model better preserves fine detail and is computationally faster, while in comparison to a localized PCA model, our model better handles variation in expression, is faster, and allows us to fix identity parameters for a given subject.
Estimation of Human Body Shape and Posture Under Clothing
Our method can estimate the body shape and posture of both static scans and motion sequences of dressed human body scans.
Analysis of Farthest Point Sampling for Approximating Geodesics in a Graph
We show that $\mathcal{F}_{FPS}$ can be bounded in terms of the minimal value $\mathcal{F}^*$ of the stretch factor obtained using an optimal placement of $k$ sources as $\mathcal{F}_{FPS}\leq 2 r_e^2 \mathcal{F}^*+ 2 r_e^2 + 8 r_e + 1$, where $r_e$ is the ratio of the lengths of the longest and the shortest edges of the graph.
A Low-Dimensional Representation for Robust Partial Isometric Correspondences Computation
In this paper, we introduce a new approach to partial, intrinsic isometric matching.
Finite Element Based Tracking of Deforming Surfaces
Our method combines the use of prior information on the geometry of the object modeled by a smooth template and the use of a linear finite element method to predict the deformation.
Review of Statistical Shape Spaces for 3D Data with Comparative Analysis for Human Faces
Due to the wide availability of databases of high-quality data, we use the human face as the specific shape we wish to extract from corrupted data.
Fully Automatic Expression-Invariant Face Correspondence
The predicted landmarks are then used to compute point-to-point correspondences between a template model and the newly available scan.
