I am an Assistant Professor at the Linköping University in the Computer Vision Lab (CVL).
My main research interests are human motion capture and 3D reconstruction. A special focus lies on the application of machine learning techniques such as deep learning, dimensionality reduction and compressed sensing. My current research analyses weakly supervised training of deep neural networks for human pose reconstruction and physics-integration into neural networks.
From 2021 to 2022 I was a PostDoc at the University of British Columbia in the group of Helge Rhodin. From 2015 to 2020 I was working as a PhD student at the Leibniz University of Hannover.
- November, 2023. Anomaly detection can not only be applied to images but also to temporal data from robots as shown in our new Transactions on Robotics paper: The voraus-AD Dataset for Anomaly Detection in Robot Applications.
- Oktober, 2023. New paper at 3DV! Check out Mirror-aware Neural Humans, a motion capture approach based on mirrors instead of multiple cameras.
- September, 2023. Our new scene flow estimator GMSF is accepted to NeurIPS!
- Juli, 2023. DiffPose is accepted to ICCV!
- October, 2022. Two new papers at WACV. AudioViewer finally made it and AST sets a new state of the art for industrial defect detection.
- September, 2022. Our paper AutoLink is accepted to NeurIPS. Amazing work by Xingzhe! Check it out here: https://xingzhehe.github.io/autolink/.
- September, 2022. I joined the Computer Vision Lab at the Linköping University as an assistant professor in September.
- March, 2022. Very successful CVPR with 3 papers accepted.
- October, 2021. Our paper Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection is accepted to WACV 2022. Code available here.
- August, 2021. Our paper Probabilistic Monocular 3D Human Pose Estimation with Normalizing Flows is accepted to ICCV 2021. The code is available at https://github.com/twehrbein/Probabilistic-Monocular-3D-Human-Pose-Estimation-with-Normalizing-Flows
- July, 2021. This summer term 2 I will be teaching DATA 311 at UBC. All information can be found in Canvas.
- February, 2021. Our paper CanonPose: Self-supervised Monocular 3D Human Pose Estimation in the Wild got accepted to CVPR 2021. Code: https://github.com/bastianwandt/CanonPose
Deep learning-based 2D keypoint detection in alpine ski racing – A performance analysis of state-of-the-art algorithms applied to regular skiing and injury situations
Michael Zwölfer, Dieter Heinrich, Kurt Schindelwig, Bastian Wandt, Helge Rhodin, Jörg Spörri, Werner Nachbauer
Region-based Cycle-Consistent Data Augmentation for Object Detection
Florian Kluger, Christoph Reinders, Kevin Raetz, Philipp Schelske, Bastian Wandt, Hanno Ackermann, Bodo Rosenhahn
BigData 2018 Road Damage Detection Challenge (Special Award)
Detail-aware Image Decomposition for an HEVC-based Texture Synthesis Framework
Bastian Wandt, Thorsten Laude, Bodo Rosenhahn, Jörn Ostermann
Machine Learning (at University of British Columbia, Okanagan Campus)
Machine Learning (at TNT, Hannover)
Bodo Rosenhahn and Bastian Wandt
Tracking and Matching in Image Sequences (at TNT, Hannover)
Bodo Rosenhahn and Bastian Wandt
Master and Bachelor theses
- Klassifikation menschlicher Bewegungen in Unterräumen (2017)
- Modellierung eines Posenraums menschlicher Bewegungen mit Hilfe neuronaler Netze (2017)
- Representation of Human Motion using Neural Networks (2018)
- Codierung menschlicher Oberflächenmodelle mittels Neuronaler Netze (2018)
- Zeitkonsistente Schätzung Menschlicher Posen mittels Neuronaler Netze (2018)
- Recurrent Neural Networks for Monocular Human Motion Capture (2019)
- Realtime 3D Human Pose Estimation (2019)
- Texturesynthesis using Generative Adversarial Networks (2019)
- Dimensionsreduktionsmethoden zur Kodierung menschlicher Posen (2019)
- Tiefenschätzung zur 3D Rekonstruktion menschlicher Posen (2019)
- Roboterkinematikbasierte Registrierung eines Augmented Reality Systems (2019)