Our project proposes an end-to-end 3D face alignment and reconstruction
network. The backbone of our model is built by Bottle-Neck structure via
Depth-wise Separable Convolution. We integrate Coordinate Attention mechanism
and Spatial Group-wise Enhancement to extract more representative features. For
more stable training process and better convergence, we jointly use Wing loss
and the Weighted Parameter Distance Cost to learn parameters for 3D Morphable
model and 3D vertices. Our proposed model outperforms all baseline models both
quantitatively and qualitatively.

提出了一个端到端的 3D 人脸对齐和重建网络，使用了瓶颈结构、坐标注意机制和 Wing 损失来提取更具代表性的特征，该模型在定量和定性方面均优于基准模型。

CSANet：稳健的 3D 人脸对齐和重建的通道空间注意力网络

CSANet: Channel Spatial Attention Network for Robust 3D Face Alignment  and Reconstruction

We present a new loss function, namely Wing loss, for robust facial landmark
localisation with Convolutional Neural Networks (CNNs). We first compare and
analyse different loss functions including L2, L1 and smooth L1. The analysis
of these loss functions suggests that, for the training of a CNN-based
localisation model, more attention should be paid to small and medium range
errors. To this end, we design a piece-wise loss function. The new loss
amplifies the impact of errors from the interval (-w, w) by switching from L1
loss to a modified logarithm function.
To address the problem of under-representation of samples with large
out-of-plane head rotations in the training set, we propose a simple but
effective boosting strategy, referred to as pose-based data balancing. In
particular, we deal with the data imbalance problem by duplicating the minority
training samples and perturbing them by injecting random image rotation,
bounding box translation and other data augmentation approaches. Last, the
proposed approach is extended to create a two-stage framework for robust facial
landmark localisation. The experimental results obtained on AFLW and 300W
demonstrate the merits of the Wing loss function, and prove the superiority of
the proposed method over the state-of-the-art approaches.

本文提出了一种新的损失函数 Wing loss，用于通过卷积神经网络实现面部关键点的鲁棒定位。通过对比 L2、L1 和平滑 L1 等不同的损失函数，并分析这些损失函数，结论是对于基于 CNN 的定位模型训练，应更加关注小和中等范围的错误。针对此问题，提出了一种分段损失函数 Wing loss，并通过姿态数据平衡的策略解决了训练集中具有大偏转的数据不足的问题。最后，将该方法扩展为一种用于鲁棒面部关键点定位的两阶段框架，并在 AFLW 和 300W 上进行了实验验证，结果表明 Wing loss 的优越性以及该方法的超越了现有技术的水平。