Embedding and generation of indoor climbing routes with variational autoencoder

2020/09/16 by Kin Ho Lo, Lo, K. H.
Computer Science · Environmental Science · #Video Analysis and Summarization #Music and Audio Processing #Remote Sensing and LiDAR Applications

paper · pdf · doi:10.48550/arxiv.2009.13271

Abstract

Recent increase in popularity of indoor climbing allows possible applications of deep learning algorthms to classify and generate climbing routes. In this work, we employ a variational autoencoder to climbing routes in a standardized training apparatus MoonBoard, a well-known training tool within the climbing community. By sampling the encoded latent space, it is observed that the algorithm can generate high quality climbing routes. 22 generated problems are uploaded to the Moonboard app for user review. This algorithm could serve as a first step to facilitate indoor climbing route setting.

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