Unlocking Human-Like Facial Expressions in Humanoid Robots: A Novel Approach for Action Unit Driven Facial Expression Disentangled Synthesis

Humanoid robots often struggle to express the intricate and authentic facial expressions characteristic of humans, potentially hampering user engagement. To address this challenge, we introduce a comprehensive two-stage methodology to empower our autonomous affective robot with the capacity to exhibit rich and natural facial expressions. In the initial stage, we present an innovative Action Unit(AU) driven facial expression disentangled synthesis method, enabling the generation of nuanced robot facial expression images guided by Action Units. By harnessing facial AUs within a framework of weakly supervised learning, we effectively surmount the scarcity of paired training data (comprising source and target facial expression images). To preserve the integrity of AUs while mitigating identity interference, we leverage a latent facial attribute space to disentangle expression-related and expression–unrelated
cues, employing solely the former for expression synthesis. In the subsequent phase, we actualize an affective robot endowed with multifaceted degrees of freedom for facial movements, facilitating the embodiment of the synthesized fine-grained facial expressions.
We devise a specialized motor command mapping network that serves as a conduit between the generated expression images and the robot’s realistic facial responses. By utilizing the physical motor positions as constraints, we refine the prediction of precise motor commands from the robot’s generated facial expressions. This refinement process ensures that the robot’s facial movements authentically express accurate and natural expressions. Finally, qualitative and quantitative evaluations on the benchmarking Emotionet dataset verify the effectiveness of the proposed generation method. Results on the self-developed affective robot indicate
that our method achieves a promising generation of specific facial expressions with given AUs, significantly enhancing the affective human-robot interaction.