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arXiv · 2407.01074

Multimodal Conditional 3D Face Geometry Generation

Christopher Otto·Prashanth Chandran·Sebastian Weiss·Markus Gross·Gaspard Zoss·Derek Bradley
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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.

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Christopher Otto, Prashanth Chandran, Sebastian Weiss, Markus Gross, Gaspard Zoss, Derek Bradley. 2024-07-01. Multimodal Conditional 3D Face Geometry Generation. https://doi.org/10.1016/j.cag.2025.104325

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