Latent Motion Manifold with Sequential Auto-Encoders (Unknown language)

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We propose the sequential autoencoders for constructing latent motion manifold. Sequential autoencoders minimize the difference between the ground truth motion space distribution and reconstructed motion space distribution sampled from the latent motion manifold. Our method is based on sequence-to-sequence model for encoding the temporal information of human motion. We also adopt Wasserstein regularizer for matching encoded training distribution to the prior distribution of motion manifold. Our experiments show that randomly sampled points from trained motion manifold distribution become natural and valid motions.

Table of contents conference proceedings

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1
Capturing Floor Exercise from Multiple Panning-Zooming Cameras
Kobayashi, D. / Yamamoto, Masanobu | 2018
3
Latent Motion Manifold with Sequential Auto-Encoders
Jang, Deok-Kyeong / Lee, Sung-Hee | 2018
5
Dilated Temporal Fully-Convolutional Network for Semantic Segmentation of Motion Capture Data
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13
Untangling Layered Garments: An Implicit Approach
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