Synthesizing Human Faces Using Latent Space Factorization and Local Weights

Minyoung Kim, Young J. Kim

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


We propose a 3D face generative model with local weights to increase the model’s variations and expressiveness. The proposed model allows partial manipulation of the face while still learning the whole face mesh. For this purpose, we address an effective way to extract local facial features from the entire data and explore a way to manipulate them during a holistic generation. First, we factorize the latent space of the whole face to the subspace indicating different parts of the face. In addition, local weights generated by non-negative matrix factorization are applied to the factorized latent space so that the decomposed part space is semantically meaningful. We experiment with our model and observe that effective facial part manipulation is possible, and that the model’s expressiveness is improved.

Original languageEnglish
Title of host publicationAdvances in Computer Graphics - 38th Computer Graphics International Conference, CGI 2021, Proceedings
EditorsNadia Magnenat-Thalmann, Nadia Magnenat-Thalmann, Victoria Interrante, Daniel Thalmann, George Papagiannakis, Bin Sheng, Jinman Kim, Marina Gavrilova
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages8
ISBN (Print)9783030890285
StatePublished - 2021
Event38th Computer Graphics International Conference, CGI 2021 - Virtual, Online
Duration: 6 Sep 202110 Sep 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13002 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference38th Computer Graphics International Conference, CGI 2021
CityVirtual, Online

Bibliographical note

Funding Information:
Acknowledgements. This project was supported in part by the ITRC/IITP program (IITP-2021-0-01460) and the NRF (2017R1A2B3012701 and 2021R1A4A1032582) in South Korea. Y.-J. Kim is the corresponding author.

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.


  • Face synthesis
  • Generative models
  • Learning-based approach


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