  We propose LookinGood^{\pi}, a novel neural re-rendering approach that is
aimed to (1) improve the rendering quality of the low-quality reconstructed
results from human performance capture system in real-time; (2) improve the
generalization ability of the neural rendering network on unseen people. Our
key idea is to utilize the rendered image of reconstructed geometry as the
guidance to assist the prediction of person-specific details from few reference
images, thus enhancing the re-rendered result. In light of this, we design a
two-branch network. A coarse branch is designed to fix some artifacts (i.e.
holes, noise) and obtain a coarse version of the rendered input, while a detail
branch is designed to predict "correct" details from the warped references. The
guidance of the rendered image is realized by blending features from two
branches effectively in the training of the detail branch, which improves both
the warping accuracy and the details' fidelity. We demonstrate that our method
outperforms state-of-the-art methods at producing high-fidelity images on
unseen people.
