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Article

Generative Model for Skeletal Human Movements Based on Conditional DC-GAN Applied to Pseudo-Images

1
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
2
Centre de Robotique, MINES ParisTech, Université PSL, 75006 Paris, France
*
Author to whom correspondence should be addressed.
Algorithms 2020, 13(12), 319; https://doi.org/10.3390/a13120319
Received: 4 November 2020 / Revised: 22 November 2020 / Accepted: 27 November 2020 / Published: 3 December 2020
(This article belongs to the Special Issue Algorithms for Human Gesture, Activity and Mobility Analysis)
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 objective 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 skeletal data provided in the large NTU_RGB+D public dataset. Our generative model can output qualitatively correct skeletal human movements for any of the 60 action classes. We also quantitatively evaluate the performance of our model by computing Fréchet inception distances, which shows strong correlation to human judgement. To the best of 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. View Full-Text
Keywords: generative model; human movement; conditional deep convolutional generative adversarial network; GAN; spatiotemporal pseudo-image; TSSI generative model; human movement; conditional deep convolutional generative adversarial network; GAN; spatiotemporal pseudo-image; TSSI
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MDPI and ACS Style

Xi, W.; Devineau, G.; Moutarde, F.; Yang, J. Generative Model for Skeletal Human Movements Based on Conditional DC-GAN Applied to Pseudo-Images. Algorithms 2020, 13, 319. https://doi.org/10.3390/a13120319

AMA Style

Xi W, Devineau G, Moutarde F, Yang J. Generative Model for Skeletal Human Movements Based on Conditional DC-GAN Applied to Pseudo-Images. Algorithms. 2020; 13(12):319. https://doi.org/10.3390/a13120319

Chicago/Turabian Style

Xi, Wang, Guillaume Devineau, Fabien Moutarde, and Jie Yang. 2020. "Generative Model for Skeletal Human Movements Based on Conditional DC-GAN Applied to Pseudo-Images" Algorithms 13, no. 12: 319. https://doi.org/10.3390/a13120319

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