Generation of 3D human models and animations using simple sketches

Date

2020-01-01

Author

Akman, Alican
Sahillioğlu, Yusuf
Sezgin, T. Metin

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Generating 3D models from 2D images or sketches is a widely studied important problem in computer graphics. We describe the first method to generate a 3D human model from a single sketched stick figure. In contrast to the existing human modeling techniques, our method requires neither a statistical body shape model nor a rigged 3D character model. We exploit Variational Autoencoders to develop a novel framework capable of transitioning from a simple 2D stick figure sketch, to a corresponding 3D human model. Our network learns the mapping between the input sketch and the output 3D model. Furthermore, our model learns the embedding space around these models. We demonstrate that our network can generate not only 3D models, but also 3D animations through interpolation and extrapolation in the learned embedding space. Extensive experiments show that our model learns to generate reasonable 3D models and animations.

Subject Keywords

Computing methodologies, Computing methodologies, Learning latent representations, Neural networks

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85086005616&origin=inward
https://hdl.handle.net/11511/97207

Conference Name

Graphics Interface 2020, GI 2020

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

A. Akman, Y. Sahillioğlu, and T. M. Sezgin, “Generation of 3D human models and animations using simple sketches,” Toronto, Virtual, Online, Kanada, 2020, vol. 2020-May, Accessed: 00, 2022. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85086005616&origin=inward.