With the comprehensive research conducted on various face analysis tasks, there is a growing interest among researchers to develop a unified approach to face perception. Existing methods mainly discuss unified representation and training, which lack task extensibility and application efficiency. To tackle this issue, we focus on the unified model structure, exploring a face generalist model. As an intuitive design, Naive Faceptor enables tasks with the same output shape and granularity to share the structural design of the standardized output head, achieving improved task extensibility. Furthermore, Faceptor is proposed to adopt a well-designed single-encoder dual-decoder architecture, allowing task-specific queries to represent new-coming semantics. This design enhances the unification of model structure while improving application efficiency in terms of storage overhead. Additionally, we introduce Layer-Attention into Faceptor, enabling the model to adaptively select features from optimal layers to perform the desired tasks. Through joint training on 13 face perception datasets, Faceptor achieves exceptional performance in facial landmark localization, face parsing, age estimation, expression recognition, binary attribute classification, and face recognition, achieving or surpassing specialized methods in most tasks. Our training framework can also be applied to auxiliary supervised learning, significantly improving performance in data-sparse tasks such as age estimation and expression recognition. The code and models will be made publicly available at https://github.com/lxq1000/Faceptor.

翻译：随着对人脸分析任务进行全面研究的深入，研究者们日益关注如何开发统一的人脸感知方法。现有方法主要探讨统一表示与训练策略，缺乏任务可扩展性与应用效率。为解决此问题，我们从统一模型结构入手，探索人脸通用模型。作为直观设计，Naive Faceptor使得输出形状与粒度相同的任务能够共享标准化输出头的结构设计，从而提升任务可扩展性。进一步地，我们提出Faceptor，采用精心设计的单编码器-双解码器架构，允许任务特定查询表征新语义。该设计在增强模型结构统一性的同时，提升了存储开销方面的应用效率。此外，我们引入层注意力机制（Layer-Attention），使模型能够自适应地从最优层选取特征以执行目标任务。通过联合训练13个人脸感知数据集，Faceptor在人脸关键点定位、人脸解析、年龄估计、表情识别、二值属性分类及人脸识别任务中均取得卓越性能，在多数任务上达到或超越专用方法。我们的训练框架亦可应用于辅助监督学习，显著提升年龄估计和表情识别等数据稀疏任务的性能。代码与模型将开源至https://github.com/lxq1000/Faceptor。