Dense 3D Coordinate Code Prior Guidance for High-Fidelity Face Swapping and Face Reenactment

Anni Tang, Han Xue, Jun Ling, Rong Xie, Li Sang

Abstract: In face synthesis tasks, commonly used 2D face representations (e.g. 2D landmarks, segmentation maps, etc.) are usually sparse and discontinuous. To combat these shortcomings, we utilize a dense and continuous representation, named Projected Normalized Coordinate Code (PNCC), as the guidance and develop a PNCC-Spatio-Normalization (PSN) method to achieve face synthesis regarding arbitrary head poses and expressions. Based on PSN, we provide an effective framework for face reenactment and face swapping task. To ensure a harmonious and seamless face swapping, a simple yet effective Appearance-Blending Module (ABM) is proposed to fit the synthesized face to the target face. Our method is subject-agnostic and can be applied to any pair of faces without extra fine-tuning. Both qualitative and quantitative experiments are conducted to demonstrate the superiority of the proposed method in comparisons to existing state-of-the-art systems.