Browsing by Subject "Machine learning"
Now showing items 1-20 of 54
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Adjoint Bijective ZoomOut: Efficient Upsampling for Learned Linearly-invariant Embedding
(The Eurographics Association, 2023)In this paper, we present a novel method for refining correspondences between 3D point clouds. Our method is compatible with the functional map framework, so it relies on the spectral representation of the correspondence. ... -
AvatarizeMe: A Fast Software Tool for Transforming Selfies into Animatable Lifelike Avatars Using Machine Learning
(The Eurographics Association, 2023)Creating realistic avatars that faithfully replicate facial features from single-input images is a challenging task in computer graphics, virtual communication, and interactive entertainment. These avatars have the potential ... -
BareSkinNet: De-makeup and De-lighting via 3D Face Reconstruction
(The Eurographics Association and John Wiley & Sons Ltd., 2022)We propose BareSkinNet, a novel method that simultaneously removes makeup and lighting influences from the face image. Our method leverages a 3D morphable model and does not require a reference clean face image or a specified ... -
Black Box Geometric Computing with Python: From Theory to Practice
(The Eurographics Association, 2020)The first part of the course is theoretical, and introduces the finite element method trough interactive Jupyter notebooks. It also covers recent advancements toward an integrated pipeline, considering meshing and element ... -
Capture and Automatic Production of Digital Humans in Real Motion with a Temporal 3D Scanner
(The Eurographics Association, 2024)The demand for virtual human characters in Extended Realities (XR) is growing across industries from entertainment to healthcare. Achieving natural behaviour in virtual environments requires digitizing real-world actions, ... -
Combining Cluster and Outlier Analysis with Visual Analytics
(The Eurographics Association, 2017)Cluster and outlier analysis are two important tasks. Due to their nature these tasks seem to be opposed to each other, i.e., data objects either belong to a cluster structure or a sparsely populated outlier region. In ... -
A Comprehensive Review of Data-Driven Co-Speech Gesture Generation
(The Eurographics Association and John Wiley & Sons Ltd., 2023)Gestures that accompany speech are an essential part of natural and efficient embodied human communication. The automatic generation of such co-speech gestures is a long-standing problem in computer animation and is ... -
Consistent Multi- and Single-View HDR-Image Reconstruction from Single Exposures
(The Eurographics Association, 2022)Recently, there have been attempts to obtain high-dynamic range (HDR) images from single exposures and efforts to reconstruct multi-view HDR images using multiple input exposures. However, there have not been any attempts ... -
Controllably Sparse Perturbations of Robust Classifiers for Explaining Predictions and Probing Learned Concepts
(The Eurographics Association, 2021)Explaining the predictions of a deep neural network (DNN) in image classification is an active area of research. Many methods focus on localizing pixels, or groups of pixels, which maximize a relevance metric for the ... -
D-Cloth: Skinning-based Cloth Dynamic Prediction with a Three-stage Network
(The Eurographics Association and John Wiley & Sons Ltd., 2023)We propose a three-stage network that utilizes a skinning-based model to accurately predict dynamic cloth deformation. Our approach decomposes cloth deformation into three distinct components: static, coarse dynamic, and ... -
DASS Good: Explainable Data Mining of Spatial Cohort Data
(The Eurographics Association and John Wiley & Sons Ltd., 2023)Developing applicable clinical machine learning models is a difficult task when the data includes spatial information, for example, radiation dose distributions across adjacent organs at risk. We describe the co-design of ... -
Data+Shift: Supporting Visual Investigation of Data Distribution Shifts by Data Scientists
(The Eurographics Association, 2022)Machine learning on data streams is increasingly more present in multiple domains. However, there is often data distribution shift that can lead machine learning models to make incorrect decisions. While there are automatic ... -
A Deep Learning Approach to No-Reference Image Quality Assessment For Monte Carlo Rendered Images
(The Eurographics Association, 2018)In Full-Reference Image Quality Assessment (FR-IQA) images are compared with ground truth images that are known to be of high visual quality. These metrics are utilized in order to rank algorithms under test on their image ... -
Deep Learning Inverse Multidimensional Projections
(The Eurographics Association, 2019)We present a new method for computing inverse projections from 2D spaces to arbitrary high-dimensional spaces. Given any projection technique, we train a deep neural network to learn a low-to-high dimensional mapping based ... -
Detecting Aliasing Artifacts in Image Sequences Using Deep Neural Networks
(ACM, 2018)In this short paper we present a machine learning approach to detect visual artifacts in rendered image sequences. Specifically, we train a deep neural network using example aliased and antialiased image sequences exported ... -
Emergence in the Expressive Machine
(The Eurographics Association, 2019)The ''Expressive Machine'' is a series of interactive artworks which explore a machine's-eye view of the world. The machine- an assemblage of hardware and software-provokes sensual interaction with viewer-participants, ... -
FACTS: Facial Animation Creation using the Transfer of Styles
(The Eurographics Association, 2024)The ability to accurately capture and express emotions is a critical aspect of creating believable characters in video games and other forms of entertainment. Traditionally, this animation has been achieved with artistic ... -
Fully Convolutional Graph Neural Networks for Parametric Virtual Try-On
(The Eurographics Association and John Wiley & Sons Ltd., 2020)We present a learning-based approach for virtual try-on applications based on a fully convolutional graph neural network. In contrast to existing data-driven models, which are trained for a specific garment or mesh topology, ... -
GANtlitz: Ultra High Resolution Generative Model for Multi-Modal Face Textures
(The Eurographics Association and John Wiley & Sons Ltd., 2024)High-resolution texture maps are essential to render photoreal digital humans for visual effects or to generate data for machine learning. The acquisition of high resolution assets at scale is cumbersome, it involves ... -
Generating Parametric BRDFs from Natural Language Descriptions
(The Eurographics Association and John Wiley & Sons Ltd., 2023)Artistic authoring of 3D environments is a laborious enterprise that also requires skilled content creators. There have been impressive improvements in using machine learning to address different aspects of generating 3D ...