Abstract
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.
Abstract (translated)
在人形机器人的面部表情模仿中，准确地复制人类的面部表情对于实现自然的人机交互至关重要。现有的大多数方法通过将二维面部特征点映射到机器人的执行器命令来实现了逼真的表情模仿，但这些方法对脸部形态的变化较为敏感，这会导致表现效果下降。在本文中，我们提出了一种独立于形态的表情模仿方法，该方法能够分离出表情与面部形态，从而消除形态的影响，并产生更真实的人形机器人面部表情。 具体来说，我们构建了一个表情解耦模块，通过自监督学习来解析并学习表情语义，即从形态表示和表情表示中将其分开。我们设计了一个表情转移模块，它将这些表示映射到机器人的执行器命令上，以感知表情错误为目标进行训练，并根据学到的表情语义生成准确的面部表情。 为了支持实验验证，我们开发了一种定制且高度表达性的机器人——“彭睿”，作为用于真实表情模仿的实验平台。大量的实验证明了我们的方法能够让这种人形机器人有效而逼真地再现一系列人类面部表情。所有机器人的代码和实现细节都将公开发布。
URL
https://arxiv.org/abs/2603.07068
