Manipulating facial poses is challenging, especially when addressing significant pose variations. While extensive research has been dedicated to address large poses and manipulate various facial expressions, this frequently results in compromised image quality. The challenge may arise from nonlinearity of the latent space. We must navigate a complex path along the high-quality image manifold and determine the optimal direction for the face rotation task, which may secure the most effective disentanglement. Moreover, the regularity of the latent space also affects directly the quality of the resulting image. In this paper, we have made a careful study of the latent space, and deliberately crafted our model to identify the complicated trajectory of rotating facial manipulation with exceptional disentanglement. Our facial pose generative model, aims at enhancing the quality of generated images while preserving the identity and fidelity and achieving better disentanglement. Data acquisition is another challenging aspect, requiring extensive preparation and meticulous setup. To address this, we suggest a flipping technique to mitigate dataset limitations. Ultimately, we strive to strike a balance between image quality and pose generation, ensuring that our results are both visually pleasing and accurately representing the desired facial pose.