Spatio-Frequency Decoupled Weak-Supervision for Face Reconstruction.

ABSTRACT: 3D face reconstruction has witnessed considerable progress in recovering 3D face shapes and textures from in-the-wild images. However, due to a lack of texture detail information, the reconstructed shape and texture based on deep learning could not be used to re-render a photorealistic facial image since it does not work in harmony with weak supervision only from the spatial domain. In the paper, we propose a method of spatio-frequency decoupled weak-supervision for face reconstruction, which applies the losses from not only the spatial domain but also the frequency domain to learn the reconstruction process that approaches photorealistic effect based on the output shape and texture. In detail, the spatial domain losses cover image-level and perceptual-level supervision. Moreover, the freq

SUBMITTER: Li Y 

PROVIDER: S-EPMC9522507 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature