Zobrazeno 1 - 10
of 1 820
pro vyhledávání: '"Bulling, A."'
We present HAIFAI - a novel collaborative human-AI system to tackle the challenging task of reconstructing a visual representation of a face that exists only in a person's mind. Users iteratively rank images presented by the AI system based on their
Externí odkaz:
http://arxiv.org/abs/2412.06323
Human hand and head movements are the most pervasive input modalities in extended reality (XR) and are significant for a wide range of applications. However, prior works on hand and head modelling in XR only explored a single modality or focused on s
Externí odkaz:
http://arxiv.org/abs/2410.16430
Recent work has highlighted the potential of modelling interactive behaviour analogously to natural language. We propose interactive behaviour summarisation as a novel computational task and demonstrate its usefulness for automatically uncovering lat
Externí odkaz:
http://arxiv.org/abs/2410.08356
High-frequency components in eye gaze data contain user-specific information promising for various applications, but existing gaze modelling methods focus on low frequencies of typically not more than 30 Hz. We present DiffEyeSyn -- the first computa
Externí odkaz:
http://arxiv.org/abs/2409.01240
Autor:
Müller, Philipp, Balazia, Michal, Baur, Tobias, Dietz, Michael, Heimerl, Alexander, Penzkofer, Anna, Schiller, Dominik, Brémond, François, Alexandersson, Jan, André, Elisabeth, Bulling, Andreas
Estimating the momentary level of participant's engagement is an important prerequisite for assistive systems that support human interactions. Previous work has addressed this task in within-domain evaluation scenarios, i.e. training and testing on t
Externí odkaz:
http://arxiv.org/abs/2408.16625
Autor:
Habermann, Daniel, Schmitt, Marvin, Kühmichel, Lars, Bulling, Andreas, Radev, Stefan T., Bürkner, Paul-Christian
Multilevel models (MLMs) are a central building block of the Bayesian workflow. They enable joint, interpretable modeling of data across hierarchical levels and provide a fully probabilistic quantification of uncertainty. Despite their well-recognize
Externí odkaz:
http://arxiv.org/abs/2408.13230
We propose MToMnet - a Theory of Mind (ToM) neural network for predicting beliefs and their dynamics during human social interactions from multimodal input. ToM is key for effective nonverbal human communication and collaboration, yet, existing metho
Externí odkaz:
http://arxiv.org/abs/2407.06762
We present MST-MIXER - a novel video dialog model operating over a generic multi-modal state tracking scheme. Current models that claim to perform multi-modal state tracking fall short of two major aspects: (1) They either track only one modality (mo
Externí odkaz:
http://arxiv.org/abs/2407.02218
We present HOIMotion - a novel approach for human motion forecasting during human-object interactions that integrates information about past body poses and egocentric 3D object bounding boxes. Human motion forecasting is important in many augmented r
Externí odkaz:
http://arxiv.org/abs/2407.02633
While numerous works have assessed the generative performance of language models (LMs) on tasks requiring Theory of Mind reasoning, research into the models' internal representation of mental states remains limited. Recent work has used probing to de
Externí odkaz:
http://arxiv.org/abs/2406.17513