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Multimodal Conditional 3D Face Geometry Generation
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DOI：10.1016/j.cag.2025.104325.png)
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Abstract
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• We present a new method for 3D face geometry generation from 6 different types of conditionings (prompts) within a single model. • We propose a comprehensive solution for training such a method from scratch, with 3D geometry data augmentations and by representing 3D geometry as position maps to better fit existing diffusion pipelines. • We show that our method supports face generation with expressions, sketch-based editing for 3D face design, stochastic variations of details conditioned on low resolution FLAME faces, generalization to in-the-wild data and dynamic face generation from videos.
Keywords:
Multimodal generation
3D face geometry
Deep learning
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