Face attribute translation with multiple feature perceptual reconstruction assisted by style translator

Abstract

Improving the accuracy and disentanglement of attribute translation, and maintaining the consistency of face identity have been hot topics in face attribute translation. Recent approaches employ attention mechanisms to enable attribute translation in facial images. However, due to the lack of accuracy in the extraction of style code, the attention mechanism alone is not precise enough for the translation of attributes. To tackle this, we introduce a style translator module, which partitions the style code into attribute-related and unrelated components, enhancing latent space disentanglement for more accurate attribute manipulation. Additionally, many current methods use per-pixel loss functions to preserve face identity. However, this can sacrifice crucial high-level features and textures in the target image. To address this limitation, we propose a multiple-perceptual reconstruction loss to better maintain image fidelity. Extensive qualitative and quantitative experiments in this article demonstrate significant improvements over state-of-the-art methods, validating the effectiveness of our approach.