Version 1
: Received: 30 October 2020 / Approved: 2 November 2020 / Online: 2 November 2020 (12:55:22 CET)
How to cite:
Xi, W.; Devineau, G.; Moutarde, F.; Yang, J. Generative Model for Skeletal Human Movements based on conditional DC-GAN applied to pseudo-images. Preprints2020, 2020110039. https://doi.org/10.20944/preprints202011.0039.v1
Xi, W.; Devineau, G.; Moutarde, F.; Yang, J. Generative Model for Skeletal Human Movements based on conditional DC-GAN applied to pseudo-images. Preprints 2020, 2020110039. https://doi.org/10.20944/preprints202011.0039.v1
Xi, W.; Devineau, G.; Moutarde, F.; Yang, J. Generative Model for Skeletal Human Movements based on conditional DC-GAN applied to pseudo-images. Preprints2020, 2020110039. https://doi.org/10.20944/preprints202011.0039.v1
APA Style
Xi, W., Devineau, G., Moutarde, F., & Yang, J. (2020). Generative Model for Skeletal Human Movements based on conditional DC-GAN applied to pseudo-images. Preprints. https://doi.org/10.20944/preprints202011.0039.v1
Chicago/Turabian Style
Xi, W., Fabien Moutarde and Jie Yang. 2020 "Generative Model for Skeletal Human Movements based on conditional DC-GAN applied to pseudo-images" Preprints. https://doi.org/10.20944/preprints202011.0039.v1
Abstract
Generative models for images, audio, text and other low-dimension data have achieved great success in recent years. Generating artificial human movements can also be useful for many applications, including improvement of data augmentation methods for human gesture recognition. The object of this research is to develop a generative model for skeletal human movement, allowing to control the action type of generated motion while keeping the authenticity of the result and the natural style variability of gesture execution. We propose to use a conditional Deep Convolutional Generative Adversarial Network (DC-GAN) applied to pseudo-images representing skeletal pose sequences using Tree Structure Skeleton Image format. We evaluate our approach on the 3D-skeleton data provided in the large NTU RGB+D public dataset. Our generative model can output qualitatively correct skeletal human movements for any of its 60 action classes. We also quantitatively evaluate the performance of our model by computing Frechet Inception Distances, which shows strong correlation to human judgement. Up to our knowledge, our work is the first successful class-conditioned generative model for human skeletal motions based on pseudo-image representation of skeletal pose sequences.
Keywords
generative model; human movement; conditional Deep Convolutional Generative Adversarial Network; GAN; spatio-temporal pseudo-image
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
