  Recent years have witnessed significant growth of face alignment. Though
dense facial landmark is highly demanded in various scenarios, e.g., cosmetic
medicine and facial beautification, most works only consider sparse face
alignment. To address this problem, we present a framework that can enrich
landmark density by existing sparse landmark datasets, e.g., 300W with 68
points and WFLW with 98 points. Firstly, we observe that the local patches
along each semantic contour are highly similar in appearance. Then, we propose
a weakly-supervised idea of learning the refinement ability on original sparse
landmarks and adapting this ability to enriched dense landmarks. Meanwhile,
several operators are devised and organized together to implement the idea.
Finally, the trained model is applied as a plug-and-play module to the existing
face alignment networks. To evaluate our method, we manually label the dense
landmarks on 300W testset. Our method yields state-of-the-art accuracy not only
in newly-constructed dense 300W testset but also in the original sparse 300W
and WFLW testsets without additional cost.
