Previous methods on estimating detailed human depth often require supervised
training with `ground truth' depth data. This paper presents a self-supervised
method that can be trained on YouTube videos without known depth, which makes
training data collection simple and improves the generalization of the learned
network. The self-supervised learning is achieved by minimizing a
photo-consistency loss, which is evaluated between a video frame and its
neighboring frames warped according to the estimated depth and the 3D non-rigid
motion of the human body. To solve this non-rigid motion, we first estimate a
rough SMPL model at each video frame and compute the non-rigid body motion
accordingly, which enables self-supervised learning on estimating the shape
details. Experiments demonstrate that our method enjoys better generalization
and performs much better on data in the wild.

该研究论文提出了一个自监督学习的方法，用于估计细节丰富的人体深度，并通过照片一致性损失以及 SMPL 模型和 3D 非刚性运动来解决非刚性运动问题，无需真实数据，更容易应用于实际数据集中，最终实验表明其性能表现优异。