no code implementations • 21 Nov 2023 • Georgios Albanis, Nikolaos Zioulis, Kostas Kolomvatsos
It solves the motion capture task in a single stage, eliminating the need for temporal smoothness objectives while still delivering smooth motions.
3D human pose and shape estimation Markerless Motion Capture
no code implementations • 25 Sep 2023 • Georgios Albanis, Nikolaos Zioulis, Spyridon Thermos, Anargyros Chatzitofis, Kostas Kolomvatsos
By relying on a unified representation, we show that training such a model is not bound to high-end MoCap training data acquisition, and exploit the advances in marker-less MoCap to acquire the necessary data.
no code implementations • 23 Apr 2023 • Nikolaos Zioulis, James F. O'Brien
It follows a predict-and-optimize approach, relying on data-driven model estimates for the constraints that will be used to solve for the body's parameters.
no code implementations • 11 Jul 2022 • Nikolaos Zioulis, Georgios Albanis, Petros Drakoulis, Federico Alvarez, Dimitrios Zarpalas, Petros Daras
In this work we introduce a biologically inspired long-range skip connection for the UNet architecture that relies on the perceptual illusion of hybrid images, being images that simultaneously encode two images.
no code implementations • 22 Jun 2022 • Nikolaos Zioulis, Federico Alvarez, Dimitrios Zarpalas, Petros Daras
Spherical cameras capture scenes in a holistic manner and have been used for room layout estimation.
no code implementations • CVPR 2022 • Anargyros Chatzitofis, Georgios Albanis, Nikolaos Zioulis, Spyridon Thermos
Traditional marker-based motion capture requires excessive and specialized equipment, hindering accessibility and wider adoption.
no code implementations • 10 Dec 2021 • Vasileios Gkitsas, Nikolaos Zioulis, Vladimiros Sterzentsenko, Alexandros Doumanoglou, Dimitrios Zarpalas
In order to acquire photo-realistic and structural consistent background, existing deep learning methods either employ image inpainting approaches or incorporate the learning of the scene layout as an individual task and leverage it later in a not fully differentiable semantic region-adaptive normalization module.
1 code implementation • 1 Dec 2021 • Georgios Albanis, Nikolaos Zioulis, Petros Drakoulis, Federico Alvarez, Dimitrios Zarpalas, Petros Daras
In this work we contribute a distribution shift benchmark for a computer vision task; monocular depth estimation.
no code implementations • 19 Oct 2021 • Anargyros Chatzitofis, Nikolaos Zioulis, Georgios Nikolaos Albanis, Dimitrios Zarpalas, Petros Daras
A series of 2D (and 3D) keypoint estimation tasks are built upon heatmap coordinate representation, i. e. a probability map that allows for learnable and spatially aware encoding and decoding of keypoint coordinates on grids, even allowing for sub-pixel coordinate accuracy.
1 code implementation • 14 Oct 2021 • Anargyros Chatzitofis, Leonidas Saroglou, Prodromos Boutis, Petros Drakoulis, Nikolaos Zioulis, Shishir Subramanyam, Bart Kevelham, Caecilia Charbonnier, Pablo Cesar, Dimitrios Zarpalas, Stefanos Kollias, Petros Daras
HUMAN4D is introduced to the computer vision and graphics research communities to enable joint research on spatio-temporally aligned pose, volumetric, mRGBD and audio data cues.
1 code implementation • 6 Sep 2021 • Georgios Albanis, Nikolaos Zioulis, Petros Drakoulis, Vasileios Gkitsas, Vladimiros Sterzentsenko, Federico Alvarez, Dimitrios Zarpalas, Petros Daras
Pano3D is a new benchmark for depth estimation from spherical panoramas.
no code implementations • 9 Feb 2021 • Konstantinos Konstantoudakis, David Breitgand, Alexandros Doumanoglou, Nikolaos Zioulis, Avi Weit, Kyriaki Christaki, Petros Drakoulis, Emmanouil Christakis, Dimitrios Zarpalas, Petros Daras
Immersive 3D media is an emerging type of media that captures, encodes and reconstructs the 3D appearance of people and objects, with applications in tele-presence, teleconference, entertainment, gaming and other fields.
Networking and Internet Architecture Multimedia
1 code implementation • 7 Feb 2021 • Nikolaos Zioulis, Federico Alvarez, Dimitrios Zarpalas, Petros Daras
In this work we show how to estimate full room layouts in a single-shot, eliminating the need for postprocessing.
1 code implementation • RC 2020 • Georgios Nikolaos Albanis, Nikolaos Zioulis, Anargyros Chatzitofis, Anastasios Dimou, Dimitrios Zarpalas, Petros Daras
We communicated with the authors of [1] through GitHub, and we would like to thank them as they provided a fast and detailed response.
no code implementations • 19 Oct 2020 • Honglin Yuan, Remco C. Veltkamp, Georgios Albanis, Nikolaos Zioulis, Dimitrios Zarpalas, Petros Daras
From captured color and depth images, we use this simulator to generate a 3D dataset which has 400 photo-realistic synthesized color-and-depth image pairs with various view angles for training, and another 100 captured and synthetic images for testing.
2 code implementations • 20 Aug 2020 • Georgios Albanis, Nikolaos Zioulis, Anastasios Dimou, Dimitrios Zarpalas, Petros Daras
In this context, the 3D localisation of the UAV assistant is an important task that can facilitate the exchange of spatial information between the user and the UAV.
Ranked #1 on Drone Pose Estimation on UAVA
1 code implementation • 16 May 2020 • Vasileios Gkitsas, Nikolaos Zioulis, Federico Alvarez, Dimitrios Zarpalas, Petros Daras
We approach this problem differently, exploiting the availability of surface geometry to employ image-based relighting as a data generator and supervision mechanism.
2 code implementations • 23 Mar 2020 • Vladimiros Sterzentsenko, Alexandros Doumanoglou, Spyridon Thermos, Nikolaos Zioulis, Dimitrios Zarpalas, Petros Daras
This is accomplished by a soft, differentiable procrustes analysis that regularizes the segmentation and achieves higher extrinsic calibration performance in expanded sensor placement configurations, while being unrestricted by the number of sensors of the volumetric capture system.
no code implementations • 24 Sep 2019 • Vasileios Gkitsas, Antonis Karakottas, Nikolaos Zioulis, Dimitrios Zarpalas, Petros Daras
Machine learning is driven by data, yet while their availability is constantly increasing, training data require laborious, time consuming and error-prone labelling or ground truth acquisition, which in some cases is very difficult or even impossible.
2 code implementations • 17 Sep 2019 • Nikolaos Zioulis, Antonis Karakottas, Dimitrios Zarpalas, Federico Alvarez, Petros Daras
This has led to the utilization of view synthesis as an indirect objective for learning depth estimation using efficient data acquisition procedures.
2 code implementations • 16 Sep 2019 • Antonis Karakottas, Nikolaos Zioulis, Stamatis Samaras, Dimitrios Ataloglou, Vasileios Gkitsas, Dimitrios Zarpalas, Petros Daras
We present a dataset of $360^o$ images of indoor spaces with their corresponding ground truth surface normal, and train a deep convolutional neural network (CNN) on the task of monocular 360 surface estimation.
1 code implementation • 3 Sep 2019 • Vladimiros Sterzentsenko, Antonis Karakottas, Alexandros Papachristou, Nikolaos Zioulis, Alexandros Doumanoglou, Dimitrios Zarpalas, Petros Daras
Multi-view capture systems are complex systems to engineer.
1 code implementation • ICCV 2019 • Vladimiros Sterzentsenko, Leonidas Saroglou, Anargyros Chatzitofis, Spyridon Thermos, Nikolaos Zioulis, Alexandros Doumanoglou, Dimitrios Zarpalas, Petros Daras
Specifically, the proposed autoencoder exploits multiple views of the same scene from different points of view in order to learn to suppress noise in a self-supervised end-to-end manner using depth and color information during training, yet only depth during inference.
1 code implementation • ECCV 2018 • Nikolaos Zioulis, Antonis Karakottas, Dimitrios Zarpalas, Petros Daras
Recent work on depth estimation up to now has only focused on projective images ignoring 360 content which is now increasingly and more easily produced.
Ranked #18 on Depth Estimation on Stanford2D3D Panoramic
no code implementations • 8 Dec 2017 • Dimitrios S. Alexiadis, Anargyros Chatzitofis, Nikolaos Zioulis, Olga Zoidi, Georgios Louizis, Dimitrios Zarpalas, Petros Daras, Senior Member, IEEE
The latest developments in 3D capturing, processing, and rendering provide means to unlock novel 3D application pathways.