Enhancing Accuracy, Diversity, and Random Input Compatibility in Face Attribute Manipulation

Recent advancements in semantic face attribute manipulation have marked significant progress, yet challenges persist regarding flexible manipulation while retaining high-accuracy reconstruction, especially given the limitations of fixed angles and layout in input facial images. To address these limitations, this paper introduces the Accurate Results, Diverse Options, and Random Input Face Attribute Manipulation Model (ADR-FACEM), a novel text-guided approach designed for nuanced and disentangled manipulation of facial attributes. This method stands out for its adaptability in attribute selection, offering a unique blend of flexibility and randomness. At the core of our proposed model lies the innovative Latent Direction Model (LDM), which leverages an adaptive nonlinear transformation trajectory. This model adeptly processes face latent codes, enabling precise manipulation of targeted attributes while preserving other facial features, all conditioned on textual descriptions. Complementing this, the Feature Distortion Alignment Model (FDAM) is intricately designed to rectify feature distortions within the image features space, thereby significantly enhancing the reconstruction quality of non-frontal images. Through comprehensive experiments, including the accuracy of facial attribute manipulation, the diversity of facial attribute manipulation options, and the inclusiveness of random unbiased input, our model ADR-FACEM demonstrates outstanding ability to maintain complex details of facial images. Quantitative comparison and qualitative analysis of nine indicators further reinforce the superiority of our method, highlighting its excellent performance in providing a wider range of choices and improvements and its compatibility with random input in the field of facial attribute manipulation.