Relightable Neural Human Assets from Multi-view Gradient Illuminations
Taotao Zhou, Kai He, Di Wu, Teng Xu, Qixuan Zhang, Kuixiang Shao, Wenzheng Chen, Lan Xu, Jingyi Yu
Abstract
Figure 1. We present UltraStage, a new dataset containing more than 2000 human assets captured under multi-view and multi-illumination settings. The high-quality images allow us to extract detailed normal, albedo, and material maps, as well as reconstruct fine geometry (left). We further propose a neural processing pipeline to interpret each capture into a neural human asset, which enables various applications like photo-realistic relighting (middle) and exquisite novel view synthesis (right). Our assets faithfully model human details, e.g., the delicate cloth wrinkles or the vivid classical fan textures.
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