**FaceXFormer: A Unified Transformer for Facial Analysis** This paper introduces *FaceXFormer*, an end-to-end unified transformer model designed to handle a comprehensive range of facial analysis tasks, including face parsing, landmark detection, head pose estimation, attribute recognition, and estimation of age, gender, race, and landmarks visibility. Unlike conventional methods that rely on task-specific designs and preprocessing techniques, *FaceXFormer* leverages a transformer-based encoder-decoder architecture where each task is treated as a learnable token, enabling the integration of multiple tasks within a single framework. The proposed parameter-efficient decoder, FaceX, processes both face and task tokens together, enhancing the model's ability to learn robust and generalized face representations across different tasks. The authors conducted extensive experiments to evaluate the effectiveness of *FaceXFormer* against state-of-the-art specialized models and previous multi-task models, both in intra-dataset and cross-dataset evaluations across multiple benchmarks. They also demonstrated the model's robustness and generalizability by handling images "in-the-wild," achieving real-time performance of 37 FPS. The paper highlights the contributions of *FaceXFormer*, including its unified framework, parameter-efficient decoder, and comprehensive experimental results, showcasing its superior performance in various facial analysis tasks.**FaceXFormer: A Unified Transformer for Facial Analysis** This paper introduces *FaceXFormer*, an end-to-end unified transformer model designed to handle a comprehensive range of facial analysis tasks, including face parsing, landmark detection, head pose estimation, attribute recognition, and estimation of age, gender, race, and landmarks visibility. Unlike conventional methods that rely on task-specific designs and preprocessing techniques, *FaceXFormer* leverages a transformer-based encoder-decoder architecture where each task is treated as a learnable token, enabling the integration of multiple tasks within a single framework. The proposed parameter-efficient decoder, FaceX, processes both face and task tokens together, enhancing the model's ability to learn robust and generalized face representations across different tasks. The authors conducted extensive experiments to evaluate the effectiveness of *FaceXFormer* against state-of-the-art specialized models and previous multi-task models, both in intra-dataset and cross-dataset evaluations across multiple benchmarks. They also demonstrated the model's robustness and generalizability by handling images "in-the-wild," achieving real-time performance of 37 FPS. The paper highlights the contributions of *FaceXFormer*, including its unified framework, parameter-efficient decoder, and comprehensive experimental results, showcasing its superior performance in various facial analysis tasks.