Search Results for author: Emre Aksan

Found 13 papers, 8 papers with code

Convolutional Autoencoders for Human Motion Infilling

1 code implementation22 Oct 2020 Manuel Kaufmann, Emre Aksan, Jie Song, Fabrizio Pece, Remo Ziegler, Otmar Hilliges

At the heart of our approach lies the idea to cast motion infilling as an inpainting problem and to train a convolutional de-noising autoencoder on image-like representations of motion sequences.

Towards End-to-end Video-based Eye-Tracking

1 code implementation ECCV 2020 Seonwook Park, Emre Aksan, Xucong Zhang, Otmar Hilliges

Estimating eye-gaze from images alone is a challenging task, in large parts due to un-observable person-specific factors.

Eye Tracking

CoSE: Compositional Stroke Embeddings

1 code implementation NeurIPS 2020 Emre Aksan, Thomas Deselaers, Andrea Tagliasacchi, Otmar Hilliges

We demonstrate qualitatively and quantitatively that our proposed approach is able to model the appearance of individual strokes, as well as the compositional structure of larger diagram drawings.

A Spatio-temporal Transformer for 3D Human Motion Prediction

no code implementations18 Apr 2020 Emre Aksan, Peng Cao, Manuel Kaufmann, Otmar Hilliges

In this paper, we propose a novel Transformer-based architecture for the task of generative modelling of 3D human motion.

Human motion prediction motion prediction

The DIDI dataset: Digital Ink Diagram data

1 code implementation20 Feb 2020 Philippe Gervais, Thomas Deselaers, Emre Aksan, Otmar Hilliges

We are releasing a dataset of diagram drawings with dynamic drawing information.

Learning Functionally Decomposed Hierarchies for Continuous Control Tasks with Path Planning

no code implementations14 Feb 2020 Sammy Christen, Lukas Jendele, Emre Aksan, Otmar Hilliges

Functional decomposition between planning and low-level control is achieved by explicitly separating the state-action spaces across the hierarchy, which allows the integration of task-relevant knowledge per layer.

Continuous Control Decision Making +1

Structured Prediction Helps 3D Human Motion Modelling

1 code implementation ICCV 2019 Emre Aksan, Manuel Kaufmann, Otmar Hilliges

This is implemented via a hierarchy of small-sized neural networks connected analogously to the kinematic chains in the human body as well as a joint-wise decomposition in the loss function.

Human motion prediction Motion Forecasting +2

STCN: Stochastic Temporal Convolutional Networks

1 code implementation ICLR 2019 Emre Aksan, Otmar Hilliges

Convolutional architectures have recently been shown to be competitive on many sequence modelling tasks when compared to the de-facto standard of recurrent neural networks (RNNs), while providing computational and modeling advantages due to inherent parallelism.

DeepWriting: Making Digital Ink Editable via Deep Generative Modeling

no code implementations25 Jan 2018 Emre Aksan, Fabrizio Pece, Otmar Hilliges

Digital ink promises to combine the flexibility and aesthetics of handwriting and the ability to process, search and edit digital text.

Style Transfer

Guiding InfoGAN with Semi-Supervision

1 code implementation14 Jul 2017 Adrian Spurr, Emre Aksan, Otmar Hilliges

In this paper we propose a new semi-supervised GAN architecture (ss-InfoGAN) for image synthesis that leverages information from few labels (as little as 0. 22%, max.

Image Generation

Learning Human Motion Models for Long-term Predictions

no code implementations10 Apr 2017 Partha Ghosh, Jie Song, Emre Aksan, Otmar Hilliges

Furthermore, we propose new evaluation protocols to assess the quality of synthetic motion sequences even for which no ground truth data exists.

Motion Capture

Learning Deep Temporal Representations for Brain Decoding

no code implementations23 Dec 2014 Orhan Firat, Emre Aksan, Ilke Oztekin, Fatos T. Yarman Vural

By employing the proposed temporal convolutional architecture with spatial pooling, raw input fMRI data is mapped to a non-linear, highly-expressive and low-dimensional feature space where the final classification is conducted.

Brain Decoding General Classification

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