3D face reconstruction plays a very important role in many real-world multimedia applications, including digital entertainment, social media, affection analysis, and person identification. The de-facto pipeline for estimating the parametric face model from an image requires to firstly detect the facial regions with landmarks, and then crop each face to feed the deep learning-based regressor. Comparing to the conventional methods performing forward inference for each detected instance independently, we suggest an effective end-to-end framework for multi-face 3D reconstruction, which is able to predict the model parameters of multiple instances simultaneously using single network inference. Our proposed approach not only greatly reduces the computational redundancy in feature extraction but also makes the deployment procedure much easier using the single network model. More importantly, we employ the same global camera model for the reconstructed faces in each image, which makes it possible to recover the relative head positions and orientations in the 3D scene. We have conducted extensive experiments to evaluate our proposed approach on the sparse and dense face alignment tasks. The experimental results indicate that our proposed approach is very promising on face alignment tasks without fully-supervision and pre-processing like detection and crop. Our implementation is publicly available at \url{https://github.com/kalyo-zjl/WM3DR}.
翻译:3D 面部重建在许多现实世界的多媒体应用中发挥着非常重要的作用,包括数字娱乐、社交媒体、情感分析、个人识别等。从图像中估算参数表象模型的脱法托管道要求首先检测有地标的面部区域,然后将每个面部作为深层学习回归器的饲料。比较对每个被检测到的反射器进行前瞻性推断的传统方法,我们建议为多面3D重建建立一个有效的端对端框架,它能够同时利用单一网络推断预测多个情况的模型参数。我们提出的方法不仅大大减少了地物提取中的计算冗余,而且使使用单一网络模型的部署程序更容易。更重要的是,我们使用相同的全球相机模型来为每个图像中重建的面部,从而有可能恢复3D 现场的相对头部位置和方向。我们进行了广泛的实验,以评价我们提议的面部协调任务的方法。实验结果表明,我们提出的方法在面部调整任务上非常有希望,而没有完全的监控和处理前方位/MMMR3M3 和作物。我们可公开实施。