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We propose LookinGood^{\pi}, a novel neural re-rendering approach that isaimed to (1) improve the rendering quality of the low-quality reconstructedresults from human performance capture system in real-time; (2) improve thegeneralization ability of the neural rendering network on unseen people. Ourkey idea is to utilize the rendered image of reconstructed geometry as theguidance to assist the prediction of person-specific details from few referenceimages, thus enhancing the re-rendered result. In light of this, we design atwo-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 detailbranch is designed to predict "correct" details from the warped references. Theguidance of the rendered image is realized by blending features from twobranches effectively in the training of the detail branch, which improves boththe warping accuracy and the details' fidelity. We demonstrate that our methodoutperforms state-of-the-art methods at producing high-fidelity images onunseen people.

LookinGood^{\pi}: Real-time Person-independent Neural Re-rendering for High-quality Human Performance Capture