Unsupervised Motion Representation Learning with Capsule Autoencoders

• URL: http://arxiv.org/abs/2110.00529v1
• Date: Fri, 1 Oct 2021 16:52:03 GMT
• Title: Unsupervised Motion Representation Learning with Capsule Autoencoders
• Authors: Ziwei Xu, Xudong Shen, Yongkang Wong, Mohan S Kankanhalli
• Abstract summary: Motion Capsule Autoencoder (MCAE) models motion in a two-level hierarchy. MCAE is evaluated on a novel Trajectory20 motion dataset and various real-world skeleton-based human action datasets.
• Score: 54.81628825371412
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose the Motion Capsule Autoencoder (MCAE), which addresses a key challenge in the unsupervised learning of motion representations: transformation invariance. MCAE models motion in a two-level hierarchy. In the lower level, a spatio-temporal motion signal is divided into short, local, and semantic-agnostic snippets. In the higher level, the snippets are aggregated to form full-length semantic-aware segments. For both levels, we represent motion with a set of learned transformation invariant templates and the corresponding geometric transformations by using capsule autoencoders of a novel design. This leads to a robust and efficient encoding of viewpoint changes. MCAE is evaluated on a novel Trajectory20 motion dataset and various real-world skeleton-based human action datasets. Notably, it achieves better results than baselines on Trajectory20 with considerably fewer parameters and state-of-the-art performance on the unsupervised skeleton-based action recognition task.

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