Towards Fast, Accurate and Stable 3D Dense Face Alignment

Existing methods of 3D dense face alignment mainly concentrate on accuracy,thus limiting the scope of their practical applications. In this paper, wepropose a novel regression framework named 3DDFA-V2 which makes a balance amongspeed, accuracy and stability. Firstly, on the basis of a lightweight backbone,we propose a meta-joint optimization strategy to dynamically regress a smallset of 3DMM parameters, which greatly enhances speed and accuracysimultaneously. To further improve the stability on videos, we present avirtual synthesis method to transform one still image to a short-video whichincorporates in-plane and out-of-plane face moving. On the premise of highaccuracy and stability, 3DDFA-V2 runs at over 50fps on a single CPU core andoutperforms other state-of-the-art heavy models simultaneously. Experiments onseveral challenging datasets validate the efficiency of our method. Pre-trainedmodels and code are available at https://github.com/cleardusk/3DDFA_V2.