Smooth Online Multiple Appropriate Facial Reaction Generation

摘要

In dyadic interactions, facial reactions are crucial for conveying an individuals' responses to their conversational partners. Individuals may exhibit varied but appropriate facial reactions (AFRs) when perceiving the same behavioral expression. Although some recent methods can already respond multiple appropriate facial reactions to the given human speaker behaviors, the AFRs generated by these methods often fail to adequately preserve crucial head motions, leading to visual jitter and unnatural transitions between generated AFR segments. In this paper, we propose a novel and generic PFLPosNet framework which addresses the aforementioned problems at both pre-processing and post-processing stages, where a new pose-aware face behavior localization method PFL is introduced to retain the head pose displacement information from the source data. In addition, the framework proposes a real-time head pose adjustment method, PosNet, to ensure continuity and smoothness in the visual output of the model when using data with correct head pose displacement. Experimental results demonstrate that our approach not only generates more coherent and natural facial reaction sequences but also significantly outperforms existing online MAFRG methods in terms of continuity and smoothness. Our code is made available at https://github.com/rainforcetime/PFLPosNet.