12 citations · 20 across the 15 of their papers we have counts for
13 papers · 1 filter
On the Skinning of Gaussian Avatars
Nikolaos Zioulis, Nikolaos Kotarelas, Georgios Albanis +2
Radiance field-based methods have recently been used to reconstruct human avatars, showing that we can significantly downscale the systems needed for creating animated human avatar…
Monocular 3D Hand Pose Estimation with Implicit Camera Alignment
Christos Pantazopoulos, Spyridon Thermos, Gerasimos Potamianos
Estimating the 3D hand articulation from a single color image is an important problem with applications in Augmented Reality (AR), Virtual Reality (VR), Human-Computer Interaction…
Towards Practical Single-shot Motion Synthesis
Konstantinos Roditakis, Spyridon Thermos, Nikolaos Zioulis
Despite the recent advances in the so-called "cold start" generation from text prompts, their needs in data and computing resources, as well as the ambiguities around intellectual…
Noise-in, Bias-out: Balanced and Real-time MoCap Solving
Georgios Albanis, Nikolaos Zioulis, Spyridon Thermos +2
Real-time optical Motion Capture (MoCap) systems have not benefited from the advances in modern data-driven modeling. In this work we apply machine learning to solve noisy unstruct…
Compositionally Equivariant Representation Learning
Xiao Liu, Pedro Sanchez, Spyridon Thermos +2
Deep learning models often need sufficient supervision (i.e. labelled data) in order to be trained effectively. By contrast, humans can swiftly learn to identify important anatomy…
HSIC-InfoGAN: Learning Unsupervised Disentangled Representations by Maximising Approximated Mutual Information
Xiao Liu, Spyridon Thermos, Pedro Sanchez +2
Learning disentangled representations requires either supervision or the introduction of specific model designs and learning constraints as biases. InfoGAN is a popular disentangle…
vMFNet: Compositionality Meets Domain-generalised Segmentation
Xiao Liu, Spyridon Thermos, Pedro Sanchez +2
Training medical image segmentation models usually requires a large amount of labeled data. By contrast, humans can quickly learn to accurately recognise anatomy of interest from m…
Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentation
Xiao Liu, Spyridon Thermos, Alison O'Neil +1
Generalising deep models to new data from new centres (termed here domains) remains a challenge. This is largely attributed to shifts in data statistics (domain shifts) between sou…
Measuring the Biases and Effectiveness of Content-Style Disentanglement
Xiao Liu, Spyridon Thermos, Gabriele Valvano +3
A recent spate of state-of-the-art semi- and un-supervised solutions disentangle and encode image "content" into a spatial tensor and image appearance or "style" into a vector, to…
A Deep Learning Approach to Object Affordance Segmentation
Spyridon Thermos, Petros Daras, Gerasimos Potamianos
Learning to understand and infer object functionalities is an important step towards robust visual intelligence. Significant research efforts have recently focused on segmenting th…
Deep Soft Procrustes for Markerless Volumetric Sensor Alignment
Vladimiros Sterzentsenko, Alexandros Doumanoglou, Spyridon Thermos +3
With the advent of consumer grade depth sensors, low-cost volumetric capture systems are easier to deploy. Their wider adoption though depends on their usability and by extension o…
Self-Supervised Deep Depth Denoising
Vladimiros Sterzentsenko, Leonidas Saroglou, Anargyros Chatzitofis +5
Depth perception is considered an invaluable source of information for various vision tasks. However, depth maps acquired using consumer-level sensors still suffer from non-negligi…
Deep Affordance-grounded Sensorimotor Object Recognition
Spyridon Thermos, Georgios Th. Papadopoulos, Petros Daras +1
It is well-established by cognitive neuroscience that human perception of objects constitutes a complex process, where object appearance information is combined with evidence about…