Morpheus: A Neural-driven Animatronic Face with Hybrid Actuation and Diverse Emotion Control
Zongzheng Zhang, Jiawen Yang, Ziqiao Peng, Meng Yang, Jianzhu Ma, Lin Cheng, Huazhe Xu, Hang Zhao, Hao Zhao

TL;DR
Morpheus introduces a hybrid-actuated animatronic face controlled by neural networks, enabling diverse and nuanced emotional expressions from speech, overcoming previous hardware and software limitations in expressive robotic faces.
Contribution
The paper presents a novel hybrid actuation design combined with neural network-based control for expressive animatronic faces, including a self-modeling network for automatic motor-to-expression mapping.
Findings
Able to generate a range of emotions from speech input
Achieves precise control of key facial features with hybrid actuation
Demonstrates nuanced, emotion-specific facial expressions
Abstract
Previous animatronic faces struggle to express emotions effectively due to hardware and software limitations. On the hardware side, earlier approaches either use rigid-driven mechanisms, which provide precise control but are difficult to design within constrained spaces, or tendon-driven mechanisms, which are more space-efficient but challenging to control. In contrast, we propose a hybrid actuation approach that combines the best of both worlds. The eyes and mouth-key areas for emotional expression-are controlled using rigid mechanisms for precise movement, while the nose and cheek, which convey subtle facial microexpressions, are driven by strings. This design allows us to build a compact yet versatile hardware platform capable of expressing a wide range of emotions. On the algorithmic side, our method introduces a self-modeling network that maps motor actions to facial landmarks,…
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Taxonomy
TopicsFace recognition and analysis · Video Surveillance and Tracking Methods
