Face anonymization aims to protect privacy by transforming facial images into realistic yet identity-obscured alternatives, ensuring both visual realism and utility for downstream computer vision tasks. For real-world applications, such as criminal investigations using CCTV footage or testimonial videos, a reversible anonymization process is essential to enable authorized re-identification of individuals. In this presentation, we discuss key challenges in achieving robust and realistic video face anonymization. We first examine the limitations of the widely used frame-by-frame paradigm, highlighting issues related to temporal inconsistency and visual artifacts. To overcome these limitations, we propose a sliding-window-based anonymization approach that enforces temporal coherence across consecutive frames. This method significantly improves the stability and realism of the generated anonymized faces. Finally, we outline the remaining challenges, including robustness to extreme poses and lighting variations, as well as maintaining coherence over long video sequences.