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.
|Title of host publication||Advances in Computer Graphics - 38th Computer Graphics International Conference, CGI 2021, Proceedings|
|Editors||Nadia Magnenat-Thalmann, Nadia Magnenat-Thalmann, Victoria Interrante, Daniel Thalmann, George Papagiannakis, Bin Sheng, Jinman Kim, Marina Gavrilova|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||8|
|State||Published - 2021|
|Event||38th Computer Graphics International Conference, CGI 2021 - Virtual, Online|
Duration: 6 Sep 2021 → 10 Sep 2021
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||38th Computer Graphics International Conference, CGI 2021|
|Period||6/09/21 → 10/09/21|
Bibliographical noteFunding 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.
© 2021, Springer Nature Switzerland AG.
- Face synthesis
- Generative models
- Learning-based approach