Dual-Generator Face Reenactment

We propose the Dual-Generator (DG) network for largepose face reenactment. Given a source face and a reference face as inputs, the DG network can generate an output face that has the same pose and expression as the reference face, and has the same identity as the source face. As most approaches do not particularly consider large-pose reenactment, the proposed approach addresses this issue by incorporating a 3D landmark detector into the framework and considering a loss function to capture visible local shape variation across large pose. The DG network consists of two modules, the ID-preserving Shape Generator (IDSG) and the Reenacted Face Generator (RFG). The IDSG encodes the 3D landmarks of the reference face into a reference landmark code, and encodes the source face into a source face code. The reference landmark code and the source face code are concatenated and decoded to a set of target landmarks that exhibits the pose and expression of the reference face and preserves the identity of the source face. The RFG is partially built on the StarGAN2 generator with modifications on the input and layer settings, and with a facial style encoder added in. Given the target landmarks made by the IDSG and the source face as inputs, the RFG generates the target face with the desired identity, pose and expression. We evaluate our approach on the RaFD, MPIE, VoxCeleb1, and VoxCeleb2 benchmarks and compare with state-of-the-art methods.