Denoising diffusion probabilistic models for image inpainting aim to add the
noise to the texture of image during the forward process and recover masked
regions with unmasked ones of the texture via the reverse denoising
process.Despite the meaningful semantics generation,the existing arts suffer
from the semantic discrepancy between masked and unmasked regions, since the
semantically dense unmasked texture fails to be completely degraded while the
masked regions turn to the pure noise in diffusion process,leading to the large
discrepancy between them.In this paper,we aim to answer how unmasked semantics
guide texture denoising process;together with how to tackle the semantic
discrepancy,to facilitate the consistent and meaningful semantics generation.To
this end,we propose a novel structure-guided diffusion model named
StrDiffusion,to reformulate the conventional texture denoising process under
structure guidance to derive a simplified denoising objective for image
inpainting,while revealing:1) the semantically sparse structure is beneficial
to tackle semantic discrepancy in early stage, while dense texture generates
reasonable semantics in late stage;2) the semantics from unmasked regions
essentially offer the time-dependent structure guidance for the texture
denoising process,benefiting from the time-dependent sparsity of the structure
semantics.For the denoising process,a structure-guided neural network is
trained to estimate the simplified denoising objective by exploiting the
consistency of the denoised structure between masked and unmasked
regions.Besides,we devise an adaptive resampling strategy as a formal criterion
as whether structure is competent to guide the texture denoising process,while
regulate their semantic correlations.Extensive experiments validate the merits
of StrDiffusion over the state-of-the-arts.Our code is available at
this https URL

基于结构引导的扩散模型在图像修复中通过添加噪音来从前向过程中对图像纹理进行修复，并通过反向去噪过程使用与图像纹理相同的未遮挡区域恢复已遮挡区域，解决了现有技术中遮挡和未遮挡区域之间的语义差异问题。