  Recently, heatmap regression methods based on 1D landmark representations
have shown prominent performance on locating facial landmarks. However,
previous methods ignored to make deep explorations on the good potentials of 1D
landmark representations for sequential and structural modeling of multiple
landmarks to track facial landmarks. To address this limitation, we propose a
Transformer architecture, namely 1DFormer, which learns informative 1D landmark
representations by capturing the dynamic and the geometric patterns of
landmarks via token communications in both temporal and spatial dimensions for
facial landmark tracking. For temporal modeling, we propose a recurrent token
mixing mechanism, an axis-landmark-positional embedding mechanism, as well as a
confidence-enhanced multi-head attention mechanism to adaptively and robustly
embed long-term landmark dynamics into their 1D representations; for structure
modeling, we design intra-group and inter-group structure modeling mechanisms
to encode the component-level as well as global-level facial structure patterns
as a refinement for the 1D representations of landmarks through token
communications in the spatial dimension via 1D convolutional layers.
Experimental results on the 300VW and the TF databases show that 1DFormer
successfully models the long-range sequential patterns as well as the inherent
facial structures to learn informative 1D representations of landmark
sequences, and achieves state-of-the-art performance on facial landmark
tracking.
