用特征生成人脸，兼顾身份一致性和属性多样性。
Vec2Face+ for Face Dataset Generation
- 从图像特征直接生成人脸，支持身份与属性的连续控制。
- 生成10K身份的VFace10K数据集，在7个真实测试集上达顶尖精度。
- 合成数据首次超越真实数据集CASIA-WebFace平均表现，适合训练人脸识别模型。
在生成用于人脸识别训练的人脸数据集时，通常认为高质量数据需具备强类间分离性与类内属性多样性。然而，现有方法在增加类内变化时忽视了身份一致性的重要性。为解决此问题，我们提出Vec2Face+，一种直接从图像特征生成图像的生成模型，可灵活控制人脸身份与属性。通过三种策略：1）采样差异显著的向量以生成分离良好的身份；2）提出AttrOP算法增强通用属性变化；3）采用基于LoRA的姿态控制生成侧脸图像，效率更高且更保身份。我们构建了包含10,000身份的VFace10K数据集，使人脸识别模型在七个真实测试集上达到当前最优性能。将规模扩展至400万和1200万图像的VFace100K与VFace300K，其在五个真实测试集上的准确率超过真实数据集CASIA-WebFace。这是首个合成数据集在平均准确率上超越CASIA-WebFace。此外，我们发现11个合成数据集中仅1个在双人验证任务上优于随机猜测（50%），且合成数据训练的模型比真实数据训练更具偏差，这对未来研究具有重要意义。代码已开源。
原文摘要 · Abstract (English)
When synthesizing identities as face recognition training data, it is generally believed that large inter-class separability and intra-class attribute variation are essential for synthesizing a quality dataset. % This belief is generally correct, and this is what we aim for. However, when increasing intra-class variation, existing methods overlook the necessity of maintaining intra-class identity consistency. % To address this and generate high-quality face training data, we propose Vec2Face+, a generative model that creates images directly from image features and allows for continuous and easy control of face identities and attributes. Using Vec2Face+, we obtain datasets with proper inter-class separability and intra-class variation and identity consistency using three strategies: 1) we sample vectors sufficiently different from others to generate well-separated identities; 2) we propose an AttrOP algorithm for increasing general attribute variations; 3) we propose LoRA-based pose control for generating images with profile head poses, which is more efficient and identity-preserving than AttrOP. % Our system generates VFace10K, a synthetic face dataset with 10K identities, which allows an FR model to achieve state-of-the-art accuracy on seven real-world test sets. Scaling the size to 4M and 12M images, the corresponding VFace100K and VFace300K datasets yield higher accuracy than the real-world training dataset, CASIA-WebFace, on five real-world test sets. This is the first time a synthetic dataset beats the CASIA-WebFace in average accuracy. In addition, we find that only 1 out of 11 synthetic datasets outperforms random guessing (\emph{i.e., 50\%}) in twin verification and that models trained with synthetic identities are more biased than those trained with real identities. Both are important aspects for future investigation. Code is available at https://github.com/HaiyuWu/Vec2Face_plus
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