The field of face manipulation and identity preservation is rapidly evolving, with a focus on developing more sophisticated and realistic methods for face replacement, morphing, and synthesis. Researchers are exploring the use of neural radiance fields, stable diffusion, and other techniques to improve the quality and realism of generated faces. Additionally, there is a growing emphasis on evaluating the effectiveness of these methods in preserving identity and detecting potential security threats. The development of new benchmark datasets and evaluation frameworks is also underway, aiming to provide more robust and reliable assessments of face manipulation technologies. Noteworthy papers in this area include: StableMorph, which introduces a novel approach to generating highly realistic morphed face images, and LiveNeRF, which achieves real-time face replacement with superior visual quality. Beyond the Pixels and Assessing Identity Leakage in Talking Face Generation also present significant contributions to the field, with the former introducing a hierarchical evaluation framework for identity preservation and the latter proposing a systematic evaluation methodology to analyze lip leakage in talking face generation.