A Graph Neural Network Approach for Temporal Mesh Blending and
  Correspondence
Aalok Gangopadhyay Abhinav Narayan Harish Prajwal Singh Shanmuganathan Raman
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.