Regression-based methods have recently shown promising results in
reconstructing human meshes from monocular images. By directly mapping raw
pixels to model parameters, these methods can produce parametric models in a
feed-forward manner via neural networks. However, minor deviation in parameters
may lead to noticeable misalignment between the estimated meshes and image
evidences. To address this issue, we propose a Pyramidal Mesh Alignment
Feedback (PyMAF) loop to leverage a feature pyramid and rectify the predicted
parameters explicitly based on the mesh-image alignment status in our deep
regressor. In PyMAF, given the currently predicted parameters, mesh-aligned
evidences will be extracted from finer-resolution features accordingly and fed
back for parameter rectification. To reduce noise and enhance the reliability
of these evidences, an auxiliary pixel-wise supervision is imposed on the
feature encoder, which provides mesh-image correspondence guidance for our
network to preserve the most related information in spatial features. The
efficacy of our approach is validated on several benchmarks, including
Human3.6M, 3DPW, LSP, and COCO, where experimental results show that our
approach consistently improves the mesh-image alignment of the reconstruction.
The project page with code and video results can be found at
this https URL

使用回归方法重建人体网格模型，通过提出的 PyMAF 循环利用特征金字塔明确校正网格和图像对齐状态，辅助像素级监督对特征编码器进行信息保留，能在多个基准测试中得出优秀的实验结果。