Towards High Fidelity Face Frontalization in the Wild

Abstract

Face frontalization refers to the process of synthesizing the frontal view of a face from a given profile. Due to self-occlusion and appearance distortion in the wild, it is extremely challenging to recover faithful high-resolution results meanwhile preserve texture details. This paper proposes a high fidelity pose in-variant model (HF-PIM) to produce photographic and identity-preserving results. HF-PIM frontalizes the profiles through a novel texture fusion warping procedure and leverages a dense correspondence field to bind the 2D and 3D surface spaces. We decompose the prerequisite of warping into dense correspondence field estimation and facial texture map recovering, which are both well addressed by deep networks. Different from those reconstruction methods relying on 3D data, we also propose adversarial residual dictionary learning to supervise facial texture map recovering with only monocular images. Furthermore, a multi-perception guided loss is proposed to address the practical misalignment between the ground truth frontal and profile faces, allowing HF-PIM to effectively utilize multiple images during training. Quantitative and qualitative evaluations on five controlled and uncontrolled databases show that the proposed method not only boosts the performance of pose-invariant face recognition but also improves the visual quality of high-resolution frontalization appearances.