Via generative adversarial training to learn a target distribution, these layer-wise subspaces automatically discover a set of "eigen-dimensions" at each layer corresponding to a set of semantic attributes or interpretable variations.
Facial attribute editing aims to manipulate attributes on the human face, e. g., adding a mustache or changing the hair color.
Generally, we human follow the roughly common aging trends, e. g., the wrinkles only tend to be more, longer or deeper.
Based on the encoder-decoder architecture, facial attribute editing is achieved by decoding the latent representation of the given face conditioned on the desired attributes.
On the other hand, by using a unified MLP cascade to examine proposals of all views in a centralized style, it provides a favorable solution for multi-view face detection with high accuracy and low time-cost.