88 citations · 226 across the 15 of their papers we have counts for
6 papers · 1 filter
Convolutional Autoencoders for Human Motion Infilling
Manuel Kaufmann, Emre Aksan, Jie Song +3
In this paper we propose a convolutional autoencoder to address the problem of motion infilling for 3D human motion data. Given a start and end sequence, motion infilling aims to c…
Towards End-to-end Video-based Eye-Tracking
Seonwook Park, Emre Aksan, Xucong Zhang +1
Estimating eye-gaze from images alone is a challenging task, in large parts due to un-observable person-specific factors. Achieving high accuracy typically requires labeled data fr…
CoSE: Compositional Stroke Embeddings
Emre Aksan, Thomas Deselaers, Andrea Tagliasacchi +1
We present a generative model for complex free-form structures such as stroke-based drawing tasks. While previous approaches rely on sequence-based models for drawings of basic obj…
A Spatio-temporal Transformer for 3D Human Motion Prediction
Emre Aksan, Manuel Kaufmann, Peng Cao +1
We propose a novel Transformer-based architecture for the task of generative modelling of 3D human motion. Previous work commonly relies on RNN-based models considering shorter for…
The DIDI dataset: Digital Ink Diagram data
Philippe Gervais, Thomas Deselaers, Emre Aksan +1
We are releasing a dataset of diagram drawings with dynamic drawing information. The dataset aims to foster research in interactive graphical symbolic understanding. The dataset wa…
Learning Functionally Decomposed Hierarchies for Continuous Control Tasks with Path Planning
Sammy Christen, Lukas Jendele, Emre Aksan +1
We present HiDe, a novel hierarchical reinforcement learning architecture that successfully solves long horizon control tasks and generalizes to unseen test scenarios. Functional d…