In this talk, I will show several learning-based approaches to tackle deformable shape correspondence problems. I will also present a completely unsupervised way of learning correspondence overcoming the need for the expensive annotated data. I will showcase these methods on a wide selection of correspondence benchmarks, where they outperform other techniques in terms of accuracy, generalization error, and efficiency.
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