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Found 37 papers, 16 papers with code
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).
Efficient Global Illumination for Morphable Models
Therefore, we propose to learn self-shadowing for Morphable Model parameters directly with a linear model.
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.
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.
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.
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.
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.
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.
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.
Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modeling
Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the (aggregate) posterior to encourage statistical independence of the latent factors.
Building 3D Morphable Models from a Single Scan
We propose a method for constructing generative models of 3D objects from a single 3D mesh.
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.
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 #4 on
3D Face Reconstruction
on NoW Benchmark
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Ranked #2 on
3D Human Pose Estimation
on 3DPW
(using extra training data)
Approximating Intersections and Differences Between Linear Statistical Shape Models Using Markov Chain Monte Carlo
In this paper, we present a new method to qualitatively compare two linear SSMs in dense correspondence by computing approximate intersection spaces and set-theoretic differences between the (hyper-ellipsoidal) allowable shape domains spanned by the models.
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.
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.
ShaRPy: Shape Reconstruction and Hand Pose Estimation from RGB-D with Uncertainty
Therefore, we propose ShaRPy, the first RGB-D Shape Reconstruction and hand Pose tracking system, which provides uncertainty estimates of the computed pose, e. g., when a finger is hidden or its estimate is inconsistent with the observations in the input, to guide clinical decision-making.
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.
RENI++ A Rotation-Equivariant, Scale-Invariant, 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.
The Sky's the Limit: Re-lightable Outdoor Scenes via a Sky-pixel Constrained Illumination Prior and Outside-In Visibility
We also introduce a novel `outside-in' method for computing differentiable sky visibility based on a neural directional distance function.
ReWaRD: Retinal Waves for Pre-Training Artificial Neural Networks Mimicking Real Prenatal Development
In this work we aim at the very beginning of our visual experience - pre- and post-natal retinal waves which suggest to be a pre-training mechanism for the primate visual system at a very early stage of development.
RANRAC: Robust Neural Scene Representations via Random Ray Consensus
We demonstrate the compatibility and potential of our solution for both photo-realistic robust multi-view reconstruction from real-world images based on neural radiance fields and for single-shot reconstruction based on light-field networks.
GBOT: Graph-Based 3D Object Tracking for Augmented Reality-Assisted Assembly Guidance
Augmented reality assembly guidance requires 6D object poses of target objects in real time.
exploreCOSMOS: Interactive Exploration of Conditional Statistical Shape Models in the Web-Browser
Statistical Shape Models of faces and various body parts are heavily used in medical image analysis, computer vision and visualization.
Exploring Multi-modal Neural Scene Representations With Applications on Thermal Imaging
Neural Radiance Fields (NeRFs) quickly evolved as the new de-facto standard for the task of novel view synthesis when trained on a set of RGB images.
Generalizable 3D Scene Reconstruction via Divide and Conquer from a Single View
We therefore propose a hybrid method following a divide-and-conquer strategy.
