Enhancing Texture Generation with High-Fidelity Using Advanced Texture
  Priors
Kuo Xu Maoyu Wang Muyu Wang Lincong Feng Tianhui Zhang Xiaoli Liu
The recent advancements in 2D generation technology have sparked a widespread
discussion on using 2D priors for 3D shape and texture content generation.
However, these methods often overlook the subsequent user operations, such as
texture aliasing and blurring that occur when the user acquires the 3D model
and simplifies its structure. Traditional graphics methods partially alleviate
this issue, but recent texture synthesis technologies fail to ensure
consistency with the original model's appearance and cannot achieve
high-fidelity restoration. Moreover, background noise frequently arises in
high-resolution texture synthesis, limiting the practical application of these
generation technologies.In this work, we propose a high-resolution and
high-fidelity texture restoration technique that uses the rough texture as the
initial input to enhance the consistency between the synthetic texture and the
initial texture, thereby overcoming the issues of aliasing and blurring caused
by the user's structure simplification operations. Additionally, we introduce a
background noise smoothing technique based on a self-supervised scheme to
address the noise problem in current high-resolution texture synthesis schemes.
Our approach enables high-resolution texture synthesis, paving the way for
high-definition and high-detail texture synthesis technology. Experiments
demonstrate that our scheme outperforms currently known schemes in
high-fidelity texture recovery under high-resolution conditions.