The automated generation of 3D human heads has been an intriguing and
challenging task for computer vision researchers. Prevailing methods synthesize
realistic avatars but with limited control over the diversity and quality of
rendered outputs and suffer from limited correlation between shape and texture
of the character. We propose a method that offers quality, diversity, control,
and realism along with explainable network design, all desirable features to
game-design artists in the domain. First, our proposed Geometry Generator
identifies disentangled latent directions and generate novel and diverse
samples. A Render Map Generator then learns to synthesize multiply high-fidelty
physically-based render maps including Albedo, Glossiness, Specular, and
Normals. For artists preferring fine-grained control over the output, we
introduce a novel Color Transformer Model that allows semantic color control
over generated maps. We also introduce quantifiable metrics called Uniqueness
and Novelty and a combined metric to test the overall performance of our model.
Demo for both shapes and textures can be found:
this https URL We will release our model along with the
synthetic dataset.

自动生成 3D 人头一直是计算机视觉研究人员的一个引人入胜且具有挑战性的任务。我们提出了一种方法，它具备质量、多样性、控制和逼真性等令游戏设计艺术家期望的功能，其中包括可解释的网络设计、无缝合成多个高保真度的基于物理的渲染贴图以及对生成贴图进行语义颜色控制的新颖色彩变换模型。