A Motion Matching-based Framework for Controllable Gesture Synthesis from Speech
Author:
Affiliation:
1. Department of Visual Computing and Artificial Intelligence, Max Planck Institute for Informatics, Germany
2. UC Davis, United States of America
Funder
European Research Council
Publisher
ACM
Reference45 articles.
1. Chaitanya Ahuja Dong Won Lee Yukiko I. Nakano and Louis-Philippe Morency. 2020. Style Transfer for Co-speech Gesture Animation: A Multi-speaker Conditional-Mixture Approach. In European Conference on Computer Vision (ECCV) Andrea Vedaldi Horst Bischof Thomas Brox and Jan-Michael Frahm (Eds.). Chaitanya Ahuja Dong Won Lee Yukiko I. Nakano and Louis-Philippe Morency. 2020. Style Transfer for Co-speech Gesture Animation: A Multi-speaker Conditional-Mixture Approach. In European Conference on Computer Vision (ECCV) Andrea Vedaldi Horst Bischof Thomas Brox and Jan-Michael Frahm (Eds.).
2. Simon Alexanderson Gustav Eje Henter Taras Kucherenko and Jonas Beskow. 2020. Style-controllable speech-driven gesture synthesis using normalising flows. Computer Graphics Forum(2020). Simon Alexanderson Gustav Eje Henter Taras Kucherenko and Jonas Beskow. 2020. Style-controllable speech-driven gesture synthesis using normalising flows. Computer Graphics Forum(2020).
3. Okan Arikan and D. A. Forsyth. 2002. Interactive Motion Generation from Examples. ACM Trans. Graph. (2002). Okan Arikan and D. A. Forsyth. 2002. Interactive Motion Generation from Examples. ACM Trans. Graph. (2002).
4. Speech2AffectiveGestures: Synthesizing Co-Speech Gestures with Generative Adversarial Affective Expression Learning
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