Recently, deep learning based approaches have shown promising results in 3D hand reconstruction from a single RGB image. These approaches can be roughly divided into model-based approaches, which are heavily dependent on the model's parameter space, and model-free approaches, which require large numbers of 3D ground truths to reduce depth ambiguity and struggle in weakly-supervised scenarios. To overcome these issues, we propose a novel probabilistic model to achieve the robustness of model-based approaches and reduced dependence on the model's parameter space of model-free approaches. The proposed probabilistic model incorporates a model-based network as a prior-net to estimate the prior probability distribution of joints and vertices. An Attention-based Mesh Vertices Uncertainty Regression (AMVUR) model is proposed to capture dependencies among vertices and the correlation between joints and mesh vertices to improve their feature representation. We further propose a learning based occlusion-aware Hand Texture Regression model to achieve high-fidelity texture reconstruction. We demonstrate the flexibility of the proposed probabilistic model to be trained in both supervised and weakly-supervised scenarios. The experimental results demonstrate our probabilistic model's state-of-the-art accuracy in 3D hand and texture reconstruction from a single image in both training schemes, including in the presence of severe occlusions.

该研究提出一种基于概率模型和注意力机制的框架来提高单幅图像中的三维手部重建精度和抗噪性，其中模型基于网络的先验概率分布和关节节点之间的依赖关系，通过关节与网格顶点之间的关联来改进特征表示，同时使用学习型自遮挡感知的手部纹理回归模型来获得高保真度的纹理重建。实验结果显示该概率模型在单张图像中实现了三维手部和纹理重建的最新技术，包括在有严重遮挡的情况下的情况。

使用遮挡感知纹理回归的概率化注意力模型进行三维手部重建