189 citations · 1.4k across the 55 of their papers we have counts for
5 papers · 1 filter
Full-Glow: Fully conditional Glow for more realistic image generation
Moein Sorkhei, Gustav Eje Henter, Hedvig Kjellström
Autonomous agents, such as driverless cars, require large amounts of labeled visual data for their training. A viable approach for acquiring such data is training a generative mode…
Robust model training and generalisation with Studentising flows
Simon Alexanderson, Gustav Eje Henter
Normalising flows are tractable probabilistic models that leverage the power of deep learning to describe a wide parametric family of distributions, all while remaining trainable u…
Moving fast and slow: Analysis of representations and post-processing in speech-driven automatic gesture generation
Taras Kucherenko, Dai Hasegawa, Naoshi Kaneko +2
This paper presents a novel framework for speech-driven gesture production, applicable to virtual agents to enhance human-computer interaction. Specifically, we extend recent deep-…
Let's Face It: Probabilistic Multi-modal Interlocutor-aware Generation of Facial Gestures in Dyadic Settings
Patrik Jonell, Taras Kucherenko, Gustav Eje Henter +1
To enable more natural face-to-face interactions, conversational agents need to adapt their behavior to their interlocutors. One key aspect of this is generation of appropriate non…
Gesticulator: A framework for semantically-aware speech-driven gesture generation
Taras Kucherenko, Patrik Jonell, Sanne van Waveren +4
During speech, people spontaneously gesticulate, which plays a key role in conveying information. Similarly, realistic co-speech gestures are crucial to enable natural and smooth i…