The estimation of 3D face shape from a single image must be robust to
variations in lighting, head pose, expression, facial hair, makeup, and
occlusions. Robustness requires a large training set of in-the-wild images,
which by construction, lack ground truth 3D shape. To train a network without
any 2D-to-3D supervision, we present RingNet, which learns to compute 3D face
shape from a single image. Our key observation is that an individual's face
shape is constant across images, regardless of expression, pose, lighting, etc.
RingNet leverages multiple images of a person and automatically detected 2D
face features. It uses a novel loss that encourages the face shape to be
similar when the identity is the same and different for different people. We
achieve invariance to expression by representing the face using the FLAME
model. Once trained, our method takes a single image and outputs the parameters
of FLAME, which can be readily animated. Additionally we create a new database
of faces `not quite in-the-wild' (NoW) with 3D head scans and high-resolution
images of the subjects in a wide variety of conditions. We evaluate publicly
available methods and find that RingNet is more accurate than methods that use
3D supervision. The dataset, model, and results are available for research
purposes at this http URL

RingNet 利用自动检测的 2D 面部特征从单张图片中学习计算 3D 面部形状，通过采用 FLAME 模型表征面部并对同一身份的面部形状进行相似性比较来实现对表情的不变性，并在 “不太野外” 的数据库上进行了评估，结果表明该方法比使用 3D 监督方法更准确。