  In this work, we propose an unsupervised method for learning dense
correspondences between shapes using a recent deep functional map framework.
Instead of depending on ground-truth correspondences or the computationally
expensive geodesic distances, we use heat kernels. These can be computed
quickly during training as the supervisor signal. Moreover, we propose a
curriculum learning strategy using different heat diffusion times which provide
different levels of difficulty during optimization without any sampling
mechanism or hard example mining. We present the results of our method on
different benchmarks which have various challenges like partiality, topological
noise and different connectivity.
