  3D face alignment is a very challenging and fundamental problem in computer
vision. Existing deep learning-based methods manually design different networks
to regress either parameters of a 3D face model or 3D positions of face
vertices. However, designing such networks relies on expert knowledge, and
these methods often struggle to produce consistent results across various face
poses. To address this limitation, we employ Neural Architecture Search (NAS)
to automatically discover the optimal architecture for 3D face alignment. We
propose a novel Multi-path One-shot Neural Architecture Search (MONAS)
framework that leverages multi-scale features and contextual information to
enhance face alignment across various poses. The MONAS comprises two key
algorithms: Multi-path Networks Unbiased Sampling Based Training and Simulated
Annealing based Multi-path One-shot Search. Experimental results on three
popular benchmarks demonstrate the superior performance of the MONAS for both
sparse alignment and dense alignment.
