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arXiv · 2303.02688

Text2Face: A Multi-Modal 3D Face Model

Will Rowan·Patrik Huber·Nick Pears·Andrew Keeling
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Abstract

We present the first 3D morphable modelling approach, whereby 3D face shape can be directly and completely defined using a textual prompt. Building on work in multi-modal learning, we extend the FLAME head model to a common image-and-text latent space. This allows for direct 3D Morphable Model (3DMM) parameter generation and therefore shape manipulation from textual descriptions. Our method, Text2Face, has many applications; for example: generating police photofits where the input is already in natural language. It further enables multi-modal 3DMM image fitting to sketches and sculptures, as well as images.

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Will Rowan, Patrik Huber, Nick Pears, Andrew Keeling. 2023-03-05. Text2Face: A Multi-Modal 3D Face Model. https://arxiv.org/abs/2303.02688

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