Collecting and labeling training data is one important step for learning-based methods because the process is time-consuming and biased. For face analysis tasks, although some generative models can be used to generate face data, they can only achieve a subset of generation diversity, reconstruction accuracy, 3D consistency, high-fidelity visual quality, and easy editability. One recent related work is the graphics-based generative method, but it can only render low realism head with high computation cost. In this paper, we propose MetaHead, a unified and full-featured controllable digital head engine, which consists of a controllable head radiance field(MetaHead-F) to super-realistically generate or reconstruct view-consistent 3D controllable digital heads and a generic top-down image generation framework LabelHead to generate digital heads consistent with the given customizable feature labels. Experiments validate that our controllable digital head engine achieves the state-of-the-art generation visual quality and reconstruction accuracy. Moreover, the generated labeled data can assist real training data and significantly surpass the labeled data generated by graphics-based methods in terms of training effect.

翻译：摘要：对于基于学习的方法而言，收集和标注训练数据是重要步骤，因为该过程耗时且易产生偏差。针对人脸分析任务，尽管部分生成模型可用于生成人脸数据，但仅能在生成多样性、重建精度、三维一致性、高保真视觉质量及易编辑性方面实现部分性能。近期相关工作是基于图形的生成方法，但该方法仅能渲染低真实感头部，且计算成本高昂。本文提出MetaHead——一种统一且功能全面的可控数字头部引擎，其包含可控头部辐射场（MetaHead-F）以超真实地生成或重建视角一致的三维可控数字头部，以及通用自上而下图像生成框架LabelHead，用于生成与给定可定制特征标签一致的数字头部。实验验证表明，我们的可控数字头部引擎在生成视觉质量和重建精度上达到当前最优水平。此外，生成的标注数据能辅助真实训练数据，并且在训练效果上显著超越基于图形方法生成的标注数据。