  In dyadic interaction, predicting the listener's facial reactions is
challenging as different reactions may be appropriate in response to the same
speaker's behaviour. This paper presents a novel framework called ReactFace
that learns an appropriate facial reaction distribution from a speaker's
behaviour rather than replicating the real facial reaction of the listener.
ReactFace generates multiple different but appropriate photo-realistic human
facial reactions by (i) learning an appropriate facial reaction distribution
representing multiple appropriate facial reactions; and (ii) synchronizing the
generated facial reactions with the speaker's verbal and non-verbal behaviours
at each time stamp, resulting in realistic 2D facial reaction sequences.
Experimental results demonstrate the effectiveness of our approach in
generating multiple diverse, synchronized, and appropriate facial reactions
from each speaker's behaviour, with the quality of the generated reactions
being influenced by the speaker's speech and facial behaviours. Our code is
made publicly available at \url{https://github.com/lingjivoo/ReactFace}.
