Head generation with diverse identities is an important task in computer vision and computer graphics, widely used in multimedia applications. However, current full head generation methods require a large number of 3D scans or multi-view images to train the model, resulting in expensive data acquisition cost. To address this issue, we propose Head3D, a method to generate full 3D heads with limited multi-view images. Specifically, our approach first extracts facial priors represented by tri-planes learned in EG3D, a 3D-aware generative model, and then proposes feature distillation to deliver the 3D frontal faces into complete heads without compromising head integrity. To mitigate the domain gap between the face and head models, we present dual-discriminators to guide the frontal and back head generation, respectively. Our model achieves cost-efficient and diverse complete head generation with photo-realistic renderings and high-quality geometry representations. Extensive experiments demonstrate the effectiveness of our proposed Head3D, both qualitatively and quantitatively.

翻译：具有多样化身份的头部生成是计算机视觉与计算机图形学领域的重要任务，广泛应用于多媒体应用中。然而，当前完整的头部生成方法需要大量三维扫描或多视角图像来训练模型，导致数据采集成本高昂。为解决这一问题，我们提出Head3D——一种利用有限多视角图像生成完整三维头部的方法。具体而言，本方法首先提取由3D感知生成模型EG3D学习的三平面所表征的面部先验，随后提出特征蒸馏技术，在保证头部完整性的前提下将三维正面人脸迁移至完整头部。为缓解面部与头部模型之间的域差异，我们引入双判别器分别引导正面与后侧头部的生成。本模型能以高性价比方式生成具有逼真渲染效果与高质量几何表示的多样化完整头部。大量定性与定量实验验证了所提Head3D方法的有效性。