Emre Aksan

Emre Aksan

PhD student
Advanced Interactive Technologies Lab, ETH Zürich

E-Mail
eaksan@inf.ethz.ch
Address
Stampfenbachstrasse 48, 8092 Zürich, Switzerland
Room
ETH Zurich, Department of Computer Science, STD, H 27

Biography

I am a PhD student at ETH Zurich, working in the Advanced Interactive Technologies lab with Professor Otmar Hilliges. I received my BSc (2013) and MSc (2015) degrees from Middle East Technical University (METU) in Computer Engineering. Prior to joining ETH, I worked on pattern analysis of functional magnetic resonance imaging at METU.


Research Interests

My research interests lie at the intersection of machine learning, human computer interaction and vision, focusing mainly perception and synthesis of human activities via generative modeling. I am currently working on deep generative temporal models in the following application domains:

If you are interested in Semester/Master project in our group, you can check our available projects. If you have your own topic related with my research, you are welcome to contact me.


Teaching


Publications

Teaser-Picture

Structured Prediction Helps 3D Human Motion Modelling

AuthorsE. Aksan*, M. Kaufmann*, O. Hilliges
In ProceedingsThe IEEE International Conference on Computer Vision (ICCV), Oct 2019
* These two authors contributed equally to this work.
ProjectCodePDFVideoBibTeX
Teaser-Picture

STCN:Stochastic Temporal Convolutional Networks

AuthorsE. Aksan, O. Hilliges
In ProceedingsInternational Conference on Learning Representations (ICLR), 2019
ProjectCodePDFBibTeX
Teaser-Picture

Deep Inertial Poser: Learning to Reconstruct Human Pose from Sparse Inertial Measurements in Real Time

AuthorsY. Huang*, M. Kaufmann*, E. Aksan, M. Black, O. Hilliges, G. Pons-Moll
In ACM Transactions on Graphics, (Proc. SIGGRAPH Asia), Nov 2018
* These two authors contributed equally to this work.
ProjectCodePDFVideoBibTeX
Teaser-Picture

DeepWriting: Making Digital Ink Editable via Deep Generative Modeling

AuthorsE. Aksan, F. Pece, O. Hilliges
In ProceedingsSIGCHI Conference on Human Factors in Computing Systems, Montréal, Canada, 2018

Honorable Mention Best Paper Award
ProjectCodePDFVideoBibTeX
Teaser-Picture

Learning human motion models for long-term predictions

AuthorsP. Ghosh, J. Song, E. Aksan, O. Hilliges
In Proceedings2017 International Conference on 3D Vision (3DV), 2017

Best Paper Award
ProjectCodePDFVideoBibTeX
Teaser-Picture

Guiding InfoGAN with Semi-Supervision

AuthorsA. Spurr, E. Aksan, O. Hilliges
In ProceedingsECML PKDD, Skopje, Macedonia, 2017
ProjectCodePDFBibTeX

Learning Deep Temporal Representations for Brain Decoding

Authors O. Firat, E.Aksan, I. Oztekin, F. T. Yarman Vural
ICML - Medical Imaging Workshop, Lille, France, July. 2015

Modelling the Brain Connectivity for Pattern Analysis

Authors I. Onal, E.Aksan, B. Velioglu, M. Ozay, O. Firat, I. Oztekin, F. T. Yarman Vural
22nd International Conference on Pattern Recognition (ICPR), Stockholm, Sweeden, Aug. 2014

Functional Networks of Anatomic Brain Regions

Authors B. Velioglu, E.Aksan, I. Onal, O. Firat, M. Ozay, F. T. Yarman Vural
13th IEEE International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC), London, UK, Aug. 2014

Large Scale Functional Connectivity for Brain Decoding

Authors O. Firat, I. Onal, E.Aksan, B. Velioglu, I. Oztekin, F. T. Yarman Vural
11th IASTED International Conference on Biomedical Engineering (BioMed), Zürich, Switzerland, June. 2014

Theses

An fMRI Segmentation Method Under Markov Random Fields for Brain Decoding — MSc Thesis, METU



Student Projects

Stochastic Temporal Convolutional Networks for Speech Enhancement, Sami Hamdan, 2019, MSc.

Representation Learning for Sketch Suggestions based on the Combination of Convolutional and Recurrent Networks, Andreas Blöchliger, 2018, MSc — Now at ELCA.

Emergence and Imitation of Locomotion in 2D and 3D Environments, Şahan Ayvaz, 2018, BSc.

Evaluation of Human Motion Models, Martin Blapp, 2018, BSc.

A Deep Learning Approach to Human Motion Sequences Infilling, Manuel Kaufmann, 2017, MSc — now PHD in our group.

Semi-supervised Information Maximising Generative Adversarial Networks, Adrian Spurr, 2016, MSc — now PHD in our group.