Search Results for author:
Found 28 papers, 10 papers with code
From Zero to Hero: Convincing with Extremely Complicated Math
Finally, they end up as a researcher, writing boring, non-impressive papers all day long because they only rely on simple mathematics.
ShaRPy: Shape Reconstruction and Hand Pose Estimation from RGB-D with Uncertainty
Despite their potential, markerless hand tracking technologies are not yet applied in practice to the diagnosis or monitoring of the activity in inflammatory musculoskeletal diseases.
NeRFtrinsic Four: An End-To-End Trainable NeRF Jointly Optimizing Diverse Intrinsic and Extrinsic Camera Parameters
Current research on the joint optimization of camera parameters and NeRF focuses on refining noisy extrinsic camera parameters and often relies on the preprocessing of intrinsic camera parameters.
BOSS: Bones, Organs and Skin Shape Model
Objective: A digital twin of a patient can be a valuable tool for enhancing clinical tasks such as workflow automation, patient-specific X-ray dose optimization, markerless tracking, positioning, and navigation assistance in image-guided interventions.
Approximating Intersections and Differences Between Statistical Shape Models
In this paper, we present a first method to compare two SSMs in dense correspondence by computing approximate intersection spaces and set-theoretic differences between the affine vector spaces spanned by the models.
PLIKS: A Pseudo-Linear Inverse Kinematic Solver for 3D Human Body Estimation
Current techniques directly regress the shape, pose, and translation of a parametric model from an input image through a non-linear mapping with minimal flexibility to any external influences.
State of the Art in Dense Monocular Non-Rigid 3D Reconstruction
3D reconstruction of deformable (or non-rigid) scenes from a set of monocular 2D image observations is a long-standing and actively researched area of computer vision and graphics.
A Lightweight Machine Learning Pipeline for LiDAR-simulation
Virtual testing is a crucial task to ensure safety in autonomous driving, and sensor simulation is an important task in this domain.
Rotation-Equivariant Conditional Spherical Neural Fields for Learning a Natural Illumination Prior
Training our model on a curated dataset of 1. 6K HDR environment maps of natural scenes, we compare it against traditional representations, demonstrate its applicability for an inverse rendering task and show environment map completion from partial observations.
Building 3D Generative Models from Minimal Data
We extend our model to a preliminary unsupervised learning framework that enables the learning of the distribution of 3D faces using one 3D template and a small number of 2D images.
Survey and Systematization of 3D Object Detection Models and Methods
Strong demand for autonomous vehicles and the wide availability of 3D sensors are continuously fueling the proposal of novel methods for 3D object detection.
Beyond Flatland: Pre-training with a Strong 3D Inductive Bias
Pre-training on large-scale databases consisting of natural images and then fine-tuning them to fit the application at hand, or transfer-learning, is a popular strategy in computer vision.
MOST-GAN: 3D Morphable StyleGAN for Disentangled Face Image Manipulation
Recent advances in generative adversarial networks (GANs) have led to remarkable achievements in face image synthesis.
Identity-Expression Ambiguity in 3D Morphable Face Models
We demonstrate that non-orthogonality of the variation in identity and expression can cause identity-expression ambiguity in 3D Morphable Models, and that in practice expression and identity are far from orthogonal and can explain each other surprisingly well.
Learning the shape of female breasts: an open-access 3D statistical shape model of the female breast built from 110 breast scans
We present the Regensburg Breast Shape Model (RBSM) -- a 3D statistical shape model of the female breast built from 110 breast scans acquired in a standing position, and the first publicly available.
Robust Model-based Face Reconstruction through Weakly-Supervised Outlier Segmentation
The main challenge is that the model fitting and the outlier segmentation are mutually dependent on each other, and need to be inferred jointly.
Ranked #2 on
3D Face Reconstruction
on NoW Benchmark
Deep Learning compatible Differentiable X-ray Projections for Inverse Rendering
First, a statistical human shape model of the human anatomy and second, a differentiable X-ray renderer.
Building 3D Morphable Models from a Single Scan
We propose a method for constructing generative models of 3D objects from a single 3D mesh.
Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modelling
Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the (aggregate) posterior to encourage statistical independence of the latent factors.
A Morphable Face Albedo Model
In this paper, we bring together two divergent strands of research: photometric face capture and statistical 3D face appearance modelling.
CZ-GEM: A FRAMEWORK FOR DISENTANGLED REPRESENTATION LEARNING
In this work, we tackle a slightly more intricate scenario where the observations are generated from a conditional distribution of some known control variate and some latent noise variate.
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.
Patient-specific Conditional Joint Models of Shape, Image Features and Clinical Indicators
We propose and demonstrate a joint model of anatomical shapes, image features and clinical indicators for statistical shape modeling and medical image analysis.
Can Synthetic Faces Undo the Damage of Dataset Bias to Face Recognition and Facial Landmark Detection?
We observe the following positive effects for face recognition and facial landmark detection tasks: 1) Priming with synthetic face images improves the performance consistently across all benchmarks because it reduces the negative effects of biases in the training data.
Training Deep Face Recognition Systems with Synthetic Data
In our experiments with an off-the-shelf face recognition software we observe the following phenomena: 1) The amount of real training data needed to train competitive deep face recognition systems can be reduced significantly.
Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems
4) We uncover a main limitation of current DCNN architectures, which is the difficulty to generalize when different identities to not share the same pose variation.
Efficient Global Illumination for Morphable Models
Therefore, we propose to learn self-shadowing for Morphable Model parameters directly with a linear model.
Morphable Face Models - An Open Framework
Non-rigid registration of faces is significant for many applications in computer vision, such as the construction of 3D Morphable face models (3DMMs).
