We have proposed a self-supervised deep learning framework for solving the mesh blending problem in scenarios where the meshes are not in correspondence. To solve this problem, we have developed Red-Blue MPNN, a novel graph neural network that processes an augmented graph to estimate the correspondence. We have designed a novel conditional refinement scheme to find the exact correspondence when certain conditions are satisfied. We further develop a graph neural network that takes the aligned meshes and the time value as input and fuses this information to process further and generate the desired result. Using motion capture datasets and human mesh designing software, we create a large-scale synthetic dataset consisting of temporal sequences of human meshes in motion. Our results demonstrate that our approach generates realistic deformation of body parts given complex inputs.

翻译：我们提出了一种自监督深度学习框架，用于解决网格之间不存在对应关系的网格融合问题。为此，我们开发了Red-Blue MPNN（一种新型图神经网络），该网络通过处理增强图来估计对应关系。我们设计了一种新颖的条件细化方案，以在满足特定条件时找到精确对应关系。此外，我们还开发了一种图神经网络，该网络以对齐后的网格和时间值作为输入，融合这些信息进行进一步处理，并生成期望结果。利用运动捕捉数据集和人体网格建模软件，我们创建了一个大规模的合成数据集，包含人体运动的时间序列网格。实验结果表明，本方法在面对复杂输入时能生成真实的人体部位形变。