39 citations · 110 across the 10 of their papers we have counts for
12 papers
RA-ClipScore: Making Generative Model Evaluation More Interpretable
Yifan Lu, Taras Kucherenko, Hedvig Kjellström +1
Generative models can produce images nearly indistinguishable from real data, yet rigorous and interpretable evaluation remains challenging. Conventional metrics such as FID provid…
Filter-adapted spatiotemporal sampling for real-time rendering
William Donnelly, Alan Wolfe, Judith Bütepage +1
Stochastic sampling techniques are ubiquitous in real-time rendering, where performance constraints force the use of low sample counts, leading to noisy intermediate results. To re…
Fashion Landmark Detection and Category Classification for Robotics
Thomas Ziegler, Judith Butepage, Michael C. Welle +3
Research on automated, image based identification of clothing categories and fashion landmarks has recently gained significant interest due to its potential impact on areas such as…
Imitating by generating: deep generative models for imitation of interactive tasks
Judith Bütepage, Ali Ghadirzadeh, Özge Öztimur Karadag +2
To coordinate actions with an interaction partner requires a constant exchange of sensorimotor signals. Humans acquire these skills in infancy and early childhood mostly by imitati…
Detect, anticipate and generate: Semi-supervised recurrent latent variable models for human activity modeling
Judith Bütepage, Danica Kragic
Successful Human-Robot collaboration requires a predictive model of human behavior. The robot needs to be able to recognize current goals and actions and to predict future activiti…
Advances in Variational Inference
Cheng Zhang, Judith Butepage, Hedvig Kjellstrom +1
Many modern unsupervised or semi-supervised machine learning algorithms rely on Bayesian probabilistic models. These models are usually intractable and thus require approximate inf…
Human-Robot Collaboration: From Psychology to Social Robotics
Judith Bütepage, Danica Kragic
With the advances in robotic technology, research in human-robot collaboration (HRC) has gained in importance. For robots to interact with humans autonomously they need active deci…
Anticipating many futures: Online human motion prediction and synthesis for human-robot collaboration
Judith Bütepage, Hedvig Kjellström, Danica Kragic
Fluent and safe interactions of humans and robots require both partners to anticipate the others' actions. A common approach to human intention inference is to model specific traje…
Deep representation learning for human motion prediction and classification
Judith Bütepage, Michael Black, Danica Kragic +1
Generative models of 3D human motion are often restricted to a small number of activities and can therefore not generalize well to novel movements or applications. In this work we…
CapriDB - Capture, Print, Innovate: A Low-Cost Pipeline and Database for Reproducible Manipulation Research
Florian T. Pokorny, Yasemin Bekiroglu, Karl Pauwels +3
We present a novel approach and database which combines the inexpensive generation of 3D object models via monocular or RGB-D camera images with 3D printing and a state of the art…
A Sensorimotor Reinforcement Learning Framework for Physical Human-Robot Interaction
Ali Ghadirzadeh, Judith Bütepage, Atsuto Maki +2
Modeling of physical human-robot collaborations is generally a challenging problem due to the unpredictive nature of human behavior. To address this issue, we present a data-effici…
Self-learning and adaptation in a sensorimotor framework
Ali Ghadirzadeh, Judith Bütepage, Danica Kragic +1
We present a general framework to autonomously achieve a task, where autonomy is acquired by learning sensorimotor patterns of a robot, while it is interacting with its environment…