Fast Text-to-3D-Aware Face Generation and Manipulation via Direct
  Cross-modal Mapping and Geometric Regularization
Jinlu Zhang Yiyi Zhou Qiancheng Zheng Xiaoxiong Du Gen Luo Jun Peng Xiaoshuai Sun Rongrong Ji
Text-to-3D-aware face (T3D Face) generation and manipulation is an emerging
research hot spot in machine learning, which still suffers from low efficiency
and poor quality. In this paper, we propose an End-to-End Efficient and
Effective network for fast and accurate T3D face generation and manipulation,
termed $E^3$-FaceNet. Different from existing complex generation paradigms,
$E^3$-FaceNet resorts to a direct mapping from text instructions to 3D-aware
visual space. We introduce a novel Style Code Enhancer to enhance cross-modal
semantic alignment, alongside an innovative Geometric Regularization objective
to maintain consistency across multi-view generations. Extensive experiments on
three benchmark datasets demonstrate that $E^3$-FaceNet can not only achieve
picture-like 3D face generation and manipulation, but also improve inference
speed by orders of magnitudes. For instance, compared with Latent3D,
$E^3$-FaceNet speeds up the five-view generations by almost 470 times, while
still exceeding in generation quality. Our code are released at
https://github.com/Aria-Zhangjl/E3-FaceNet.