Towards Fast, Accurate and Stable 3D Dense Face Alignment

Code

• github.com/cleardusk/3DDFA_V2
  OfficialIn paperpytorch★ 3,115
• github.com/cleardusk/3DDFA
  In paperpytorch★ 3,682
• github.com/scoutant/face-blur
  pytorch★ 6

Abstract

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

Tasks

Benchmark Results

Reproductions