Multimodal Conditional 3D Face Geometry Generation
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 39 of its 52 references that are in the map. First posted on arXiv 1 Jul 2024.
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References · 39
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27 more
- 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,131
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