TY - GEN
T1 - Synthesizing Human Faces Using Latent Space Factorization and Local Weights
AU - Kim, Minyoung
AU - Kim, Young J.
N1 - 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.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Face synthesis
KW - Generative models
KW - Learning-based approach
UR - http://www.scopus.com/inward/record.url?scp=85118320593&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-89029-2_31
DO - 10.1007/978-3-030-89029-2_31
M3 - Conference contribution
AN - SCOPUS:85118320593
SN - 9783030890285
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 398
EP - 405
BT - Advances in Computer Graphics - 38th Computer Graphics International Conference, CGI 2021, Proceedings
A2 - Magnenat-Thalmann, Nadia
A2 - Magnenat-Thalmann, Nadia
A2 - Interrante, Victoria
A2 - Thalmann, Daniel
A2 - Papagiannakis, George
A2 - Sheng, Bin
A2 - Kim, Jinman
A2 - Gavrilova, Marina
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 6 September 2021 through 10 September 2021
ER -