Semantics-Guided Object Removal for Facial Images: with Broad Applicability and Robust Style Preservation
Object removal and image inpainting in facial images is a task in which objects that occlude a facial image are specifically targeted, removed, and replaced by a properly reconstructed facial image. Two different approaches utilizing U-net and modulated generator respectively have been widely endors...
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Zusammenfassung: | Object removal and image inpainting in facial images is a task in which
objects that occlude a facial image are specifically targeted, removed, and
replaced by a properly reconstructed facial image. Two different approaches
utilizing U-net and modulated generator respectively have been widely endorsed
for this task for their unique advantages but notwithstanding each method's
innate disadvantages. U-net, a conventional approach for conditional GANs,
retains fine details of unmasked regions but the style of the reconstructed
image is inconsistent with the rest of the original image and only works
robustly when the size of the occluding object is small enough. In contrast,
the modulated generative approach can deal with a larger occluded area in an
image and provides {a} more consistent style, yet it usually misses out on most
of the detailed features. This trade-off between these two models necessitates
an invention of a model that can be applied to any size of mask while
maintaining a consistent style and preserving minute details of facial
features. Here, we propose Semantics-Guided Inpainting Network (SGIN) which
itself is a modification of the modulated generator, aiming to take advantage
of its advanced generative capability and preserve the high-fidelity details of
the original image. By using the guidance of a semantic map, our model is
capable of manipulating facial features which grants direction to the
one-to-many problem for further practicability. |
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DOI: | 10.48550/arxiv.2209.14479 |