@inproceedings{10.1016/j.cag.2025.104325,
author = {Otto, Christopher and Chandran, Prashanth and Weiss, Sebastian and Gross, Markus and Zoss, Gaspard and Bradley, Derek},
title = {Multimodal Conditional 3D Face Geometry Generation},
year = {2025},
isbn = {0097-8493},
publisher = {Elsevier},
address = {},
url = https://doi.org/10.1016/j.cag.2025.104325},
doi = {10.1016/j.cag.2025.104325},
abstract = {We present a new method for multimodal conditional 3D face geometry generation that allows user-friendly control over the output identity and expression via a number of different conditioning signals. Within a single model, we demonstrate 3D faces generated from artistic sketches, portrait photos, Canny edges, FLAME face model parameters, 2D face landmarks, or text prompts. Our approach is based on a diffusion process that generates 3D geometry in a 2D parameterized UV domain. Geometry generation passes each conditioning signal through a set of cross-attention layers (IP-Adapter), one set for each user-defined conditioning signal. The result is an easy-to-use 3D face generation tool that produces topology-consistent, high-quality geometry with fine-grain user control.},
booktitle = {Computers \& Graphics},
articleno = {132},
numpages = {1-10},
keywords = {Multimodal generation, 3D face geometry, Deep learning},
location = {
},
series = {Shape Modeling International 2025}
}