English

Text2Face: A Multi-Modal 3D Face Model

Computer Vision and Pattern Recognition 2023-03-09 v2

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.

Keywords

Cite

@article{arxiv.2303.02688,
  title  = {Text2Face: A Multi-Modal 3D Face Model},
  author = {Will Rowan and Patrik Huber and Nick Pears and Andrew Keeling},
  journal= {arXiv preprint arXiv:2303.02688},
  year   = {2023}
}

Comments

Fixed formatting and a typo

R2 v1 2026-06-28T09:02:05.573Z