In this paper, we investigate the potential of image-to-image translation (I2I) techniques for transferring realism to 3D-rendered facial images in the context of Face Recognition (FR) systems. The primary motivation for using 3D-rendered facial images lies in their ability to circumvent the challenges associated with collecting large real face datasets for training FR systems. These images are generated entirely by 3D rendering engines, facilitating the generation of synthetic identities. However, it has been observed that FR systems trained on such synthetic datasets underperform when compared to those trained on real datasets, on various FR benchmarks. In this work, we demonstrate that by transferring the realism to 3D-rendered images (i.e., making the 3D-rendered images look more real), we can boost the performance of FR systems trained on these more photorealistic images. This improvement is evident when these systems are evaluated against FR benchmarks utilizing real-world data, thereby paving new pathways for employing synthetic data in real-world applications.

翻译：本文研究了在Face Recognition（FR）系统中，利用图像到图像转换（I2I）技术将真实感迁移至3D渲染人脸图像的潜力。使用3D渲染人脸图像的主要动机在于其能够规避为训练FR系统收集大规模真实人脸数据集所面临的挑战。这些图像完全由3D渲染引擎生成，便于合成身份的创建。然而，已有研究表明，在各种FR基准测试中，基于此类合成数据集训练的FR系统性能逊于基于真实数据集训练的系统。本工作中，我们证明通过对3D渲染图像进行真实感迁移（即使3D渲染图像看起来更真实），能够提升基于这些更具照片真实感图像训练的FR系统的性能。当这些系统在利用真实世界数据的FR基准上进行评估时，该改进效果显著，从而为在现实应用中运用合成数据开辟了新途径。