Recognition and Synthesis of Object Transport Motion
- URL: http://arxiv.org/abs/2009.12967v1
- Date: Sun, 27 Sep 2020 22:13:26 GMT
- Title: Recognition and Synthesis of Object Transport Motion
- Authors: Connor Daly
- Abstract summary: This project illustrates how deep convolutional networks can be used, alongside specialized data augmentation techniques, on a small motion capture dataset.
The project shows how these same augmentation techniques can be scaled up for use in the more complex task of motion synthesis.
By exploring recent developments in the concept of Generative Adversarial Models (GANs), specifically the Wasserstein GAN, this project outlines a model that is able to successfully generate lifelike object transportation motions.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep learning typically requires vast numbers of training examples in order
to be used successfully. Conversely, motion capture data is often expensive to
generate, requiring specialist equipment, along with actors to generate the
prescribed motions, meaning that motion capture datasets tend to be relatively
small. Motion capture data does however provide a rich source of information
that is becoming increasingly useful in a wide variety of applications, from
gesture recognition in human-robot interaction, to data driven animation.
This project illustrates how deep convolutional networks can be used,
alongside specialized data augmentation techniques, on a small motion capture
dataset to learn detailed information from sequences of a specific type of
motion (object transport). The project shows how these same augmentation
techniques can be scaled up for use in the more complex task of motion
synthesis.
By exploring recent developments in the concept of Generative Adversarial
Models (GANs), specifically the Wasserstein GAN, this project outlines a model
that is able to successfully generate lifelike object transportation motions,
with the generated samples displaying varying styles and transport strategies.
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