Papers › Unsupervised Motion Representation Learning with Capsule Autoencoders
Unsupervised Motion Representation Learning with Capsule Autoencoders
Ziwei Xu, Xudong Shen, Yongkang Wong, Mohan S Kankanhalli
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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Code
Syntology Ran 15 of 20 code samples harvested from 1 repository linked to this paper; 5 have no recorded run. Of those that ran: 1 ran · honoured contract; 2 ran · violated contract; 4 ran · our draft was wrong; 3 ran · fixture could not drive it; 5 ran with no contract checked.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Self-Supervised Human Action Recognition | NTU RGB+D 120 | MCAE | Classifier | FC | #4 of 8 | Archive leaderboard | report |
| Self-Supervised Human Action Recognition | NTU RGB+D 120 | MCAE | Encoder | MCAE | #4 of 8 | Archive leaderboard | report |
| Self-Supervised Human Action Recognition | NTU RGB+D 120 | MCAE | xset (%) | 54.7 | #4 of 8 | Archive leaderboard | report |
| Self-Supervised Human Action Recognition | NTU RGB+D 120 | MCAE | xsub (%) | 52.8 | #4 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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