形態に依存しない人間型ロボットの表情模倣
Morphology-Independent Facial Expression Imitation for Human-Face Robots
人間型ロボットの表情模倣において、顔の形態の影響を排除し、表情と形態を分離して学習することで、より自然で正確な表情を再現する手法を提案した。
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著者: Xu Chen, Rui Gao, Che Sun, Zhehang Liu, Yuwei Wu, Shuo Yang, Yunde Jia
分類: cs.RO
原文アブストラクト
Accurate facial expression imitation on human-face robots is crucial for achieving natural human-robot interaction. Most existing methods have achieved photorealistic expression imitation through mapping 2D facial landmarks to a robot's actuator commands. Their imitation of landmark trajectories is susceptible to interference from facial morphology, which would lead to a performance drop. In this paper, we propose a morphology-independent expression imitation method that decouples expressions from facial morphology to eliminate morphological influence and produce more realistic expressions for human-face robots. Specifically, we construct an expression decoupling module to learn expression semantics by disentangling the expression representation from the morphology representation in a self-supervised manner. We devise an expression transfer module to map the representations to the robot's actuator commands through a learning objective of perceiving expression errors, producing accurate facial expressions based on the learned expression semantics. To support experimental validation, a custom-designed and highly expressive human-face robot, namely Pengrui, is developed to serve as an experimental platform for realistic expression imitation. Extensive experiments demonstrate that our method enables the human-face robot to reproduce a wide range of human-like expressions effectively. All code and implementation details of the robot will be released.
