Hiroyasu Akada

Hi, I am Hiroyasu Akada (朱田 浩康).
I am a Ph.D. student in the Visual Computing and Artificial Intelligence Department at Max Planck Institute for Informatics with my supervisor Dr. Vladislav Golyanik and Prof. Christian Theobalt.

Previously, I obtained my MS and BS at Keio University, with Prof. Masaki Takahashi. During my MS, I had a great opportunity to work with Prof. Peter Wonka at KAUST.
Also, I went to University of California, Berkeley to study Business Administration and spent a great time at Tencent in California, USA as an intern.

E-mail: hakada@mpi-inf.mpg.de
Google Scholar  /  Twitter /  Facebook /  Linkedin /  Github

profile photo

[Jul 2022] Paper "UnrealEgo" got accepted to ECCV 2022.
[Oct 2021] Paper Self-Supervised Learning of Domain Invariant Features for Depth Estimation got accepted to WACV 2022.
[May 2021] Paper Dynamic Object Removal from Unpaired Images for Autonomous Agricultural Robots got accepted to IAS-16.


My research focus lies at the intersection of Computer Vision and Deep Learning. In particular, I am interested in Egocentric 3D Human Pose Estimation, 3D Object Reconstruction, and Synthetic-to-Real Domain Adaptation.

UnrealEgo: A New Dataset for Robust Egocentric 3D Human Motion Capture
Hiroyasu Akada, Jian Wang, Soshi Shimada, Masaki Takahashi, Christian Theobalt, and Vladislav Golyanik
European Conference on Computer Vision (ECCV), 2022
[Project page] [Paper] [Code]

We present UnrealEgo, i.e. a new large-scale naturalistic dataset for egocentric 3D human pose estimation. UnrealEgo is based on an advanced concept of eyeglasses equipped with two fisheye cameras that can be used in unconstrained environments. UnrealEgo is the first dataset to provide in-the-wild stereo images with the largest variety of motions among existing egocentric datasets.

Self-Supervised Learning of Domain Invariant Features for Depth Estimation
Hiroyasu Akada, Shariq Farooq Bhat, Ibraheem Alhashim, Peter Wonka
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022
[Paper] [Code]

We tackle the problem of unsupervised synthetic-to-real domain adaptation for single image depth estimation. An essential building block of single image depth estimation is an encoder-decoder task network that takes RGB images as input and produces depth maps as output. In this paper, we propose a novel training strategy to force the task network to learn domain invariant representations in a self-supervised manner.

Dynamic Object Removal from Unpaired Images for Agricultural Autonomous Robots
Hiroyasu Akada and Masaki Takahashi
International Conference on Intelligent Autonomous Systems (IAS), 2021

We developed a GAN-based stem that remove dynamic objects in images. The system can be trained without using paired images with/without the dynamic objects.

Research & Work Experience

[Sep 2022 - Present] Ph.D. student, Max Planck Institute for Informatics (Saarbrucken, Germany)
[July 2021 - Aug 2022] Visiting researcher, Max Planck Institute for Informatics (Saarbrucken, Germany)
[Apr 2017 - Aug 2022] Student researcher, Keio University (Kanagawa, Japan)
[Sep 2020 - June 2021] Research internship, KAUST (Saudi Arabia, remote)
[July 2019 - Aug 2019] Business internship, Tencent (Palo Alto, CA, USA)

Awards & Grants

Nakajima Foundation (中島記念国際交流財団), 2022-2027 (expected)
CREST, Japan Science and Technology Agency, 2021-2022
TOBITATE, Ministry of Education, Culture, Sports, Science and Technology, Japan 2018-2019

Professional Services & Activities

Lab Visits:

Conference Participation:
  ECCV 2022 (Tel Aviv, Israel), WACV 2022 (Hawaii, USA), IAS (Singapore, online)

© Hiroyasu Akada 2022 / Design: jonbarron