TexRO: Generating Delicate Textures of 3D Models by Recursive
  Optimization
Jinbo Wu Xing Liu Chenming Wu Xiaobo Gao Jialun Liu Xinqi Liu Chen Zhao Haocheng Feng Errui Ding Jingdong Wang
This paper presents TexRO, a novel method for generating delicate textures of
a known 3D mesh by optimizing its UV texture. The key contributions are
two-fold. We propose an optimal viewpoint selection strategy, that finds the
most miniature set of viewpoints covering all the faces of a mesh. Our
viewpoint selection strategy guarantees the completeness of a generated result.
We propose a recursive optimization pipeline that optimizes a UV texture at
increasing resolutions, with an adaptive denoising method that re-uses existing
textures for new texture generation. Through extensive experimentation, we
demonstrate the superior performance of TexRO in terms of texture quality,
detail preservation, visual consistency, and, notably runtime speed,
outperforming other current methods. The broad applicability of TexRO is
further confirmed through its successful use on diverse 3D models.