Search Results for author:
Found 23 papers, 4 papers with code
TexTile: A Differentiable Metric for Texture Tileability
We introduce TexTile, a novel differentiable metric to quantify the degree upon which a texture image can be concatenated with itself without introducing repeating artifacts (i. e., the tileability).
SMPLitex: A Generative Model and Dataset for 3D Human Texture Estimation from Single Image
We propose SMPLitex, a method for estimating and manipulating the complete 3D appearance of humans captured from a single image.
How Will It Drape Like? Capturing Fabric Mechanics from Depth Images
We propose a method to estimate the mechanical parameters of fabrics using a casual capture setup with a depth camera.
PERGAMO: Personalized 3D Garments from Monocular Video
Clothing plays a fundamental role in digital humans.
HandFlow: Quantifying View-Dependent 3D Ambiguity in Two-Hand Reconstruction with Normalizing Flow
Through this, we demonstrate the quality of our probabilistic reconstruction and show that explicit ambiguity modeling is better-suited for this challenging problem.
SNUG: Self-Supervised Neural Dynamic Garments
We present a self-supervised method to learn dynamic 3D deformations of garments worn by parametric human bodies.
A Survey on Intrinsic Images: Delving Deep Into Lambert and Beyond
Intrinsic imaging or intrinsic image decomposition has traditionally been described as the problem of decomposing an image into two layers: a reflectance, the albedo invariant color of the material; and a shading, produced by the interaction between light and geometry.
RGB2Hands: Real-Time Tracking of 3D Hand Interactions from Monocular RGB Video
Moreover, we demonstrate that our approach offers previously unseen two-hand tracking performance from RGB, and quantitatively and qualitatively outperforms existing RGB-based methods that were not explicitly designed for two-hand interactions.
Real-time Pose and Shape Reconstruction of Two Interacting Hands With a Single Depth Camera
We present a novel method for real-time pose and shape reconstruction of two strongly interacting hands.
Self-Supervised Collision Handling via Generative 3D Garment Models for Virtual Try-On
We propose a new generative model for 3D garment deformations that enables us to learn, for the first time, a data-driven method for virtual try-on that effectively addresses garment-body collisions.
Fully Convolutional Graph Neural Networks for Parametric Virtual Try-On
Then, after a mesh topology optimization step where we generate a sufficient level of detail for the input garment type, we further deform the mesh to reproduce deformations caused by the target body shape.
SoftSMPL: Data-driven Modeling of Nonlinear Soft-tissue Dynamics for Parametric Humans
We present SoftSMPL, a learning-based method to model realistic soft-tissue dynamics as a function of body shape and motion.
Learning-Based Animation of Clothing for Virtual Try-On
We propose a model that separates global garment fit, due to body shape, from local garment wrinkles, due to both pose dynamics and body shape.
BRDF Estimation of Complex Materials with Nested Learning
The estimation of the optical properties of a material from RGB-images is an important but extremely ill-posed problem in Computer Graphics.
GANerated Hands for Real-time 3D Hand Tracking from Monocular RGB
We address the highly challenging problem of real-time 3D hand tracking based on a monocular RGB-only sequence.
VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera
A real-time kinematic skeleton fitting method uses the CNN output to yield temporally stable 3D global pose reconstructions on the basis of a coherent kinematic skeleton.
Ranked #16 on
Pose Estimation
on Leeds Sports Poses
Real-time Hand Tracking under Occlusion from an Egocentric RGB-D Sensor
We present an approach for real-time, robust and accurate hand pose estimation from moving egocentric RGB-D cameras in cluttered real environments.
EgoCap: Egocentric Marker-less Motion Capture with Two Fisheye Cameras (Extended Abstract)
Marker-based and marker-less optical skeletal motion-capture methods use an outside-in arrangement of cameras placed around a scene, with viewpoints converging on the center.
Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision
We propose a CNN-based approach for 3D human body pose estimation from single RGB images that addresses the issue of limited generalizability of models trained solely on the starkly limited publicly available 3D pose data.
Ranked #17 on
Pose Estimation
on Leeds Sports Poses
Model-based Outdoor Performance Capture
We propose a new model-based method to accurately reconstruct human performances captured outdoors in a multi-camera setup.
Real-time Joint Tracking of a Hand Manipulating an Object from RGB-D Input
However, due to difficult occlusions, fast motions, and uniform hand appearance, jointly tracking hand and object pose is more challenging than tracking either of the two separately.
EgoCap: Egocentric Marker-less Motion Capture with Two Fisheye Cameras
We therefore propose a new method for real-time, marker-less and egocentric motion capture which estimates the full-body skeleton pose from a lightweight stereo pair of fisheye cameras that are attached to a helmet or virtual reality headset.
General Automatic Human Shape and Motion Capture Using Volumetric Contour Cues
Our method uses a new image formation model with analytic visibility and analytically differentiable alignment energy.
