TRRS-DM: Two-stage Resampling and Residual Shifting for high-fidelity texture inpainting of Terracotta Warriors utilizing Diffusion Models

Abstract

As a UNESCO World Heritage Site, the Terracotta Warriors face degradation from natural erosion. Traditional restoration is time-consuming, while computer-aided methods provide efficient digital solutions. We propose a Two-stage Resampling and Residual Shifting framework using Diffusion Models ($\mathrm{TRRS\; -\; DM}$) for texture inpainting. The ResampleDiff module enhances details via perception-weighted learning and lightweight diffusion. The RefineDiff module refines results in latent space by removing noise. Experiments demonstrate that TRRS-DM achieves faster computation, surpasses existing methods in visual quality, and effectively restores damaged artifacts. This approach advances digital heritage restoration and providing scalable supports for archaeological conservation. Our code is available at https://github.com/Emwew/TRRS-DM.