This work targets high-fidelity GAN inversion with better recovered spatial details. For this purpose, we propose to involve the padding space of the generator to complement the native latent space. Concretely, we replace the constant padding (e.g., usually zeros) used in convolution layers with some instance-aware coefficients. In this way, the inductive bias assumed in the pre-trained model can be appropriately adapted to fit each individual image. We demonstrate that such a space extension allows a more flexible image manipulation, such as the separate control of face contour and facial details, and enables a novel editing manner where users can customize their own manipulations highly efficiently.

Our method allows (a) high-fidelity GAN inversion with better spatial details, and enables two novel applications including (b) face blending and (c) customizing manipulations with one image pair. More inversion results on human faces, outdoor churches, and indoor bedrooms. More face blending results by borrowing face contour from one image and facial details from another. More customized editing results, where each manipulation is defined by a simple image pair. Such a pair can be created highly efficiently either by graphics editors (like Photoshop) or by the convenient copy-and-paste.