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

154 citations · 313 across the 26 of their papers we have counts for

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cs.HC20242 cited

Towards a GENEA Leaderboard -- an Extended, Living Benchmark for Evaluating and Advancing Conversational Motion Synthesis

Rajmund Nagy, Hendric Voss, Youngwoo Yoon +7

Current evaluation practices in speech-driven gesture generation lack standardisation and focus on aspects that are easy to measure over aspects that actually matter. This leads to…

cs.HC2024

Fake it to make it: Using synthetic data to remedy the data shortage in joint multimodal speech-and-gesture synthesis

Shivam Mehta, Anna Deichler, Jim O'Regan +4

Although humans engaged in face-to-face conversation simultaneously communicate both verbally and non-verbally, methods for joint and unified synthesis of speech audio and co-speec…

cs.HC2023

The GENEA Challenge 2023: A large scale evaluation of gesture generation models in monadic and dyadic settings

Taras Kucherenko, Rajmund Nagy, Youngwoo Yoon +4

This paper reports on the GENEA Challenge 2023, in which participating teams built speech-driven gesture-generation systems using the same speech and motion dataset, followed by a…

cs.HC2023

Evaluating gesture generation in a large-scale open challenge: The GENEA Challenge 2022

Taras Kucherenko, Pieter Wolfert, Youngwoo Yoon +4

This paper reports on the second GENEA Challenge to benchmark data-driven automatic co-speech gesture generation. Participating teams used the same speech and motion dataset to bui…

cs.HC202115 cited

Integrated Speech and Gesture Synthesis

Siyang Wang, Simon Alexanderson, Joakim Gustafson +3

Text-to-speech and co-speech gesture synthesis have until now been treated as separate areas by two different research communities, and applications merely stack the two technologi…

cs.HC202123 cited

Speech2Properties2Gestures: Gesture-Property Prediction as a Tool for Generating Representational Gestures from Speech

Taras Kucherenko, Rajmund Nagy, Patrik Jonell +3

We propose a new framework for gesture generation, aiming to allow data-driven approaches to produce more semantically rich gestures. Our approach first predicts whether to gesture…