  Facial actions are spatio-temporal signals by nature, and therefore their
modeling is crucially dependent on the availability of temporal information. In
this paper, we focus on inferring such temporal dynamics of facial actions when
no explicit temporal information is available, i.e. from still images. We
present a novel approach to capture multiple scales of such temporal dynamics,
with an application to facial Action Unit (AU) intensity estimation and
dimensional affect estimation. In particular, 1) we propose a framework that
infers a dynamic representation (DR) from a still image, which captures the
bi-directional flow of time within a short time-window centered at the input
image; 2) we show that we can train our method without the need of explicitly
generating target representations, allowing the network to represent dynamics
more broadly; and 3) we propose to apply a multiple temporal scale approach
that infers DRs for different window lengths (MDR) from a still image. We
empirically validate the value of our approach on the task of frame ranking,
and show how our proposed MDR attains state of the art results on BP4D for AU
intensity estimation and on SEMAINE for dimensional affect estimation, using
only still images at test time.
