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20172026
most citedAnalyzing Input and Output Representations for Speech-Driven Gesture Generation

154 citations · 315 across the 30 of their papers we have counts for

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cs.CV2026

The GENEA Challenge 2026: A Large-Scale Disentangled Evaluation of Speech-Driven Gesture Generation on the Seamless Interaction Dataset

Rajmund Nagy, Silvia Arellano García, Hendric Voss +4

This preprint presents the results of the fourth GENEA Challenge, a large-scale human evaluation of five speech-driven gesture-generation systems trained by participating teams on…

cs.CV2020

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…

cs.CV2020

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-…

cs.CV2020

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…

cs.CV201718 cited

Consensus-based Sequence Training for Video Captioning

Sang Phan, Gustav Eje Henter, Yusuke Miyao +1

Captioning models are typically trained using the cross-entropy loss. However, their performance is evaluated on other metrics designed to better correlate with human assessments.…