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 this https URL

本文提出了一种名为 3DDFA-V2 的新颖回归框架，基于轻量级骨干和元联合优化策略，实现了速度、准确性和稳定性之间的平衡，并提出了虚拟合成方法，使得模型在高准确率和稳定性的前提下，可以在单个 CPU 核心上运行超过 50fps，并在几个具有挑战性的数据集上验证了该方法的有效性。