A Graph Neural Network Approach for Temporal Mesh Blending and Correspondence
This addresses mesh blending for human motion in graphics, but appears incremental as it builds on existing graph neural network methods.
The paper tackles the problem of mesh blending without correspondence by proposing a self-supervised deep learning framework, resulting in realistic deformation of body parts for complex inputs.
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.