Browsing by Subject "machine learning"
Now showing items 1-17 of 17
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3D scene analysis through non-visual cues
(University College London, 2019-10-06)The wide applicability of scene analysis from as few viewpoints as possible attracts the attention of many scientific fields, ranging from augmented reality to autonomous driving and robotics. When approaching 3D problems ... -
Air Quality Temporal Analyser: Interactive Temporal Analyses with Visual Predictive Assessments
(The Eurographics Association, 2021)This work presents Air Quality Temporal Analyser (AQTA), an interactive system to support visual analyses of air quality data with time. This interactive AQTA allows the seamless integration of predictive models and detailed ... -
Collaborative filtering of color aesthetics
(ACM, 2014)This paper investigates individual variation in aesthetic preferences, and learns models for predicting the preferences of individual users. Preferences for color aesthetics are learned using a collaborative filtering ... -
Data-driven models of 3D avatars and clothing for virtual try-on
(2022-07)Clothing plays a fundamental role in our everyday lives. When we choose clothing to buy or wear, we guide our decisions based on a combination of fit and style. For this reason, the majority of clothing is purchased at ... -
Data‐Driven Shape Analysis and Processing
(© 2017 The Eurographics Association and John Wiley & Sons Ltd., 2017)Data‐driven methods serve an increasingly important role in discovering geometric, structural and semantic relationships between shapes. In contrast to traditional approaches that process shapes in isolation of each other, ... -
Detail-driven Geometry Processing Pipeline using Neural Networks
(ETH Research Collection, 2022-01)Geometry processing is an established field in computer graphics, covering a variety of topics that embody decades-long research. However, with the pressing demand of reality digitization arising in recent years, classic ... -
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 ... -
Efficient Light-Transport Simulation Using Machine Learning
(ETH Zürich, 2019)The goal in this dissertation is the efficient synthesis of photorealistic images on a computer. Currently, by far the most popular approach for photorealistic image synthesis is path tracing, a Monte Carlo simulation of ... -
HardVis: Visual Analytics to Handle Instance Hardness Using Undersampling and Oversampling Techniques
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023)Despite the tremendous advances in machine learning (ML), training with imbalanced data still poses challenges in many real‐world applications. Among a series of diverse techniques to solve this problem, sampling algorithms ... -
The high dynamic range imaging pipeline: Tone-mapping, distribution, and single-exposure reconstruction
(Linköping University Electronic Press, 2018-06-08)Techniques for high dynamic range (HDR) imaging make it possible to capture and store an increased range of luminances and colors as compared to what can be achieved with a conventional camera. This high amount of image ... -
Learning an Inverse Rig Mapping for Character Animation
(ACM Siggraph, 2015)We propose a general, real-time solution to the inversion of the rig function - the function which maps animation data from a character's rig to its skeleton. Animators design character movements in the space of an animation ... -
Machine Learning For Plausible Gesture Generation From Speech For Virtual Humans
(Trinity College Dublin, The University of Dublin, 2021-08-03)The growing use of virtual humans in an array of applications such as games, human-computer interfaces, and virtual reality demands the design of appealing and engaging characters, while minimizing the cost and time of ... -
Neural Mesh Reconstruction
(Simon Fraser University, 2023-06-16)Deep learning has revolutionized the field of 3D shape reconstruction, unlocking new possibilities and achieving superior performance compared to traditional methods. However, despite being the dominant 3D shape representation ... -
Neural UpFlow: A Scene Flow Learning Approach to Increase the Apparent Resolution of Particle-Based Liquids
(ACM, 2021)We present a novel up-resing technique for generating high-resolution liquids based on scene flow estimation using deep neural networks. Our approach infers and synthesizes small- and large-scale details solely from a ... -
Real-time 3D Hand Reconstruction in Challenging Scenes from a Single Color or Depth Camera
(2020)Hands are one of the main enabling factors for performing complex tasks and humans naturally use them for interactions with their environment. Reconstruction and digitization of 3D hand motion opens up many possibilities ... -
Real-time 3D Human Body Pose Estimation from Monocular RGB Input
(Saarländische Universitäts-und Landesbibliothek, 2020-10)Human motion capture finds extensive application in movies, games, sports and biomechanical analysis. However, existing motion capture solutions require cumbersome external and/or on-body instrumentation, or use active ... -
Subspace Neural Physics: Fast Data-Driven Interactive Simulation
(ACM, 2019)Data-driven methods for physical simulation are an attractive option for interactive applications due to their ability to trade precomputation and memory footprint in exchange for improved runtime performance. Yet, existing ...